Neural Network and its applications - Revision history

https://wiki.itcollege.ee/index.php?action=history&feed=atom&title=Neural_Network_and_its_applications Neural Network and its applications - Revision history 2026-05-05T13:26:39Z Revision history for this page on the wiki MediaWiki 1.45.1 https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137496&oldid=prev Elkurr at 09:29, 2 May 2021 2021-05-02T09:29:04Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:29, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l85">Line 85:</td> <td colspan="2" class="diff-lineno">Line 85:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>(bottom). <ins style="font-weight: bold; text-decoration: none;"><ref name="maps">https://arxiv.org/pdf/1802.07257.pdf</ref></ins>]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137495&oldid=prev Elkurr at 09:26, 2 May 2021 2021-05-02T09:26:49Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:26, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l83">Line 83:</td> <td colspan="2" class="diff-lineno">Line 83:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Neural Networks application in forecasting natural hazards== </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Neural Networks application in forecasting natural hazards== </div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">===ANN application in prediction and management of natural disasters===</del></div></td><td colspan="2" class="diff-side-added"></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">===ANN application in prediction and management of natural disasters===</ins></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137494&oldid=prev Elkurr at 09:25, 2 May 2021 2021-05-02T09:25:55Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:25, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l84">Line 84:</td> <td colspan="2" class="diff-lineno">Line 84:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Neural Networks application in forecasting natural hazards== </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Neural Networks application in forecasting natural hazards== </div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></del></div></td><td colspan="2" class="diff-side-added"></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|400px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></ins></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Prediction of natural hazards with Neural Networks=== </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Prediction of natural hazards with Neural Networks=== </div></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137493&oldid=prev Elkurr at 09:24, 2 May 2021 2021-05-02T09:24:54Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:24, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l86">Line 86:</td> <td colspan="2" class="diff-lineno">Line 86:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<del style="font-weight: bold; text-decoration: none;">380px</del>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<ins style="font-weight: bold; text-decoration: none;">400px</ins>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137492&oldid=prev Elkurr at 09:23, 2 May 2021 2021-05-02T09:23:56Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:23, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l86">Line 86:</td> <td colspan="2" class="diff-lineno">Line 86:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<del style="font-weight: bold; text-decoration: none;">420px</del>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<ins style="font-weight: bold; text-decoration: none;">380px</ins>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137491&oldid=prev Elkurr at 09:23, 2 May 2021 2021-05-02T09:23:15Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:23, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l86">Line 86:</td> <td colspan="2" class="diff-lineno">Line 86:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<del style="font-weight: bold; text-decoration: none;">450px</del>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<ins style="font-weight: bold; text-decoration: none;">420px</ins>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137490&oldid=prev Elkurr at 09:22, 2 May 2021 2021-05-02T09:22:42Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:22, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l86">Line 86:</td> <td colspan="2" class="diff-lineno">Line 86:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<del style="font-weight: bold; text-decoration: none;">500px</del>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.JPG|<ins style="font-weight: bold; text-decoration: none;">450px</ins>|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>  </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137489&oldid=prev Elkurr at 09:21, 2 May 2021 2021-05-02T09:21:40Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:21, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l86">Line 86:</td> <td colspan="2" class="diff-lineno">Line 86:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.<del style="font-weight: bold; text-decoration: none;">jpg</del>|500px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:8nn.<ins style="font-weight: bold; text-decoration: none;">JPG</ins>|500px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>(bottom).]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137488&oldid=prev Elkurr at 09:20, 2 May 2021 2021-05-02T09:20:22Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:20, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l85">Line 85:</td> <td colspan="2" class="diff-lineno">Line 85:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===ANN application in prediction and management of natural disasters===</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Disaster prediction based on ANN model can be a crucial part of pre-disaster management phase for disasters of climatic origin including flooding or drought. As disastrous flooding can be triggered by various factors such as rainfall, snowmelt, high tidal waves or failure of the river blockages, sufficient dataset on areas prone to flooding is required for successful forecasts. Data from the base sensors including temperature, humidity, rain fall, wind speed and under ground water level is used as input. ANN is previously trained with the use of selected training database consisting of flooding occurrences and non-occurrences. ANN forecasts probability of either occurrence or non-occurrence of flooding by processing the climatic data of examined area as an input. Test evaluation results are statistically precise. <ref name="flood">https://www.researchgate.net/publication/4185987_A_neural_network_based_prediction_model_for_flood_in_a_disaster_management_system_with_sensor_networks/</ref></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[File:8nn.jpg|500px|thumb|Figure 4. The hazard zone map of Stanzer Valley (top) alongside with the prediction of the neuronal network</ins></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">(bottom).]]</ins></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Prediction of natural hazards with Neural Networks=== </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Prediction of natural hazards with Neural Networks=== </div></td></tr> <tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l151">Line 151:</td> <td colspan="2" class="diff-lineno">Line 154:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nowadays deepfake was already used many times to fake political propaganda. One of the most recent examples is a misleading speech by the prime minister of Belgium Sophie Wilmès. As a result, the video generated was featuring Sophie Wilmès claiming false statements about COVID-19. <ref name="brusselstimes">https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nowadays deepfake was already used many times to fake political propaganda. One of the most recent examples is a misleading speech by the prime minister of Belgium Sophie Wilmès. As a result, the video generated was featuring Sophie Wilmès claiming false statements about COVID-19. <ref name="brusselstimes">https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/</ref></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[File:7nn.png|600px|thumb|Figure <del style="font-weight: bold; text-decoration: none;">4</del>. Deepfakes are generated by autoencoder.]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[File:7nn.png|600px|thumb|Figure <ins style="font-weight: bold; text-decoration: none;">5</ins>. Deepfakes are generated by autoencoder.]]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Deepfakes generating technology===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Deepfakes generating technology===</div></td></tr> </table>

Elkurr https://wiki.itcollege.ee/index.php?title=Neural_Network_and_its_applications&diff=137486&oldid=prev Elkurr at 09:14, 2 May 2021 2021-05-02T09:14:48Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:14, 2 May 2021</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l150">Line 150:</td> <td colspan="2" class="diff-lineno">Line 150:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Politics====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Politics====</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nowadays deepfake was already used many times to fake political propaganda. One of the most recent examples is a misleading speech by the prime minister of Belgium Sophie Wilmès. As a result, the video generated was featuring Sophie Wilmès claiming false statements about COVID-19. <ref name="brusselstimes">https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nowadays deepfake was already used many times to fake political propaganda. One of the most recent examples is a misleading speech by the prime minister of Belgium Sophie Wilmès. As a result, the video generated was featuring Sophie Wilmès claiming false statements about COVID-19. <ref name="brusselstimes">https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/</ref></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[File:7nn.png|600px|thumb|Figure 4. Deepfakes are generated by autoencoder.]]</ins></div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Deepfakes generating technology===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Deepfakes generating technology===</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Autoencoders====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Autoencoders====</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">[[File:7nn.png|600px|thumb|Figure 4. Deepfakes are generated by autoencoder.]]</del></div></td><td colspan="2" class="diff-side-added"></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Deepfakes nowadays are mainly generated by one type of neural network which is called autoencoder. This exact algorithm performs two tasks. At the first stage, it takes an input image and encodes it into a small set of numerical values. It encodes the image until reaching the bottleneck layer which contains the target number of variables needed to decode the image. The second stage is the reverse process of decoding variables from the bottleneck to recreate the original input image. Basically, the autoencoder itself is an encoder-decoder algorithm that you can train. To train the mechanism we need to provide the autoencoder with as many as possible images of one human’s face, for the perfect result we need to include pictures from all different angles and various lighting conditions. Ideally, we have to feed the algorithm with up to a couple of thousands of pictures. The autoencoder then will eventually find a way to encode one’s facial features in a smaller and smaller set of variables to make the process of decoding faster and more accurate. <ref name="bdtechtalks">https://bdtechtalks.com/2020/09/04/what-is-deepfake/</ref> <ref name="kdnuggets">https://www.kdnuggets.com/2018/03/exploring-deepfakes.html/2</ref></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Deepfakes nowadays are mainly generated by one type of neural network which is called autoencoder. This exact algorithm performs two tasks. At the first stage, it takes an input image and encodes it into a small set of numerical values. It encodes the image until reaching the bottleneck layer which contains the target number of variables needed to decode the image. The second stage is the reverse process of decoding variables from the bottleneck to recreate the original input image. Basically, the autoencoder itself is an encoder-decoder algorithm that you can train. To train the mechanism we need to provide the autoencoder with as many as possible images of one human’s face, for the perfect result we need to include pictures from all different angles and various lighting conditions. Ideally, we have to feed the algorithm with up to a couple of thousands of pictures. The autoencoder then will eventually find a way to encode one’s facial features in a smaller and smaller set of variables to make the process of decoding faster and more accurate. <ref name="bdtechtalks">https://bdtechtalks.com/2020/09/04/what-is-deepfake/</ref> <ref name="kdnuggets">https://www.kdnuggets.com/2018/03/exploring-deepfakes.html/2</ref></div></td></tr> </table>

Elkurr