#floodsusceptibility — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #floodsusceptibility, aggregated by home.social.
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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
--
“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
--
“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
--
“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
--
“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- shared paper
--
H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
--
“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement -
The Growing Threat of Flooding on Transportation Infrastructure Across Texas Through 2100
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https://doi.org/10.1029/2026EF008207 <--shared paper
--
H/T @Rakibul Ahasan
“[The researchers] modeled flood susceptibility across Texas at 30 m resolution and projected how it shifts through 2100. The headline is not just that flood risk grows, but that it moves, into places current planning and regulatory maps are not watching. The July 2025 Kerrville flooding sat squarely inside the kind of inland hazard expansion this model projects.
KEY TAKEAWAYS:
● 95% of new flood exposure by 2100 is inland, away from the coast, shifting the resilience problem into interior river basins that planning has historically deprioritized.
● Where [they] benchmarked against FEMA's National Flood Hazard Layer, the model flags substantial hidden risk in rapidly urbanizing peri-urban areas, most notably in Greater Houston.
● Climate change alone expands the flood-susceptible footprint by 10–12% by 2100, before any new road or land-use development, so this is a conservative floor, not a ceiling.
● Half the state's roads and rail and 80% of its bridges already sit in flood-susceptible zones today.
● [They] accounted for both factor-importance and spatial-scale uncertainty, using a Monte Carlo weight-perturbation ensemble and multiscale analysis across nested neighborhoods.
The practical takeaway: this is a statewide screening layer, not a replacement for site-level hydraulic studies. It shows planners and policymakers where the gap between today's protection and tomorrow's risk is widest, and where unmapped peri-urban growth is walking into exposure that regulatory maps still call safe…”
#water #hydrology #hydrography #extremeweather #flood #flooding #Texas #TX #USA #transportation #infrastructure #humanimpacts #risk #hazard #cost #economics #floodsusceptibility #GIS #spatial #mapping #raster #elevation #modeling #model #spatialanalysis #planning #regulation #warning #Kerrville #hazardmapping #floodexposure #inland #coast #urban #urbanisation #development #growth #Houston #lowlying #climatechange #landuse #development #geostatstics #MonteCarlo #regionalscreening #naturalhazard #infrastructureresilience #floodmapping #hydrogeomorphology #geomorphometry #aginginfrastructure -
The Growing Threat of Flooding on Transportation Infrastructure Across Texas Through 2100
--
https://doi.org/10.1029/2026EF008207 <--shared paper
--
H/T @Rakibul Ahasan
“[The researchers] modeled flood susceptibility across Texas at 30 m resolution and projected how it shifts through 2100. The headline is not just that flood risk grows, but that it moves, into places current planning and regulatory maps are not watching. The July 2025 Kerrville flooding sat squarely inside the kind of inland hazard expansion this model projects.
KEY TAKEAWAYS:
● 95% of new flood exposure by 2100 is inland, away from the coast, shifting the resilience problem into interior river basins that planning has historically deprioritized.
● Where [they] benchmarked against FEMA's National Flood Hazard Layer, the model flags substantial hidden risk in rapidly urbanizing peri-urban areas, most notably in Greater Houston.
● Climate change alone expands the flood-susceptible footprint by 10–12% by 2100, before any new road or land-use development, so this is a conservative floor, not a ceiling.
● Half the state's roads and rail and 80% of its bridges already sit in flood-susceptible zones today.
● [They] accounted for both factor-importance and spatial-scale uncertainty, using a Monte Carlo weight-perturbation ensemble and multiscale analysis across nested neighborhoods.
The practical takeaway: this is a statewide screening layer, not a replacement for site-level hydraulic studies. It shows planners and policymakers where the gap between today's protection and tomorrow's risk is widest, and where unmapped peri-urban growth is walking into exposure that regulatory maps still call safe…”
#water #hydrology #hydrography #extremeweather #flood #flooding #Texas #TX #USA #transportation #infrastructure #humanimpacts #risk #hazard #cost #economics #floodsusceptibility #GIS #spatial #mapping #raster #elevation #modeling #model #spatialanalysis #planning #regulation #warning #Kerrville #hazardmapping #floodexposure #inland #coast #urban #urbanisation #development #growth #Houston #lowlying #climatechange #landuse #development #geostatstics #MonteCarlo #regionalscreening #naturalhazard #infrastructureresilience #floodmapping #hydrogeomorphology #geomorphometry #aginginfrastructure -
The Growing Threat of Flooding on Transportation Infrastructure Across Texas Through 2100
--
https://doi.org/10.1029/2026EF008207 <--shared paper
--
H/T @Rakibul Ahasan
“[The researchers] modeled flood susceptibility across Texas at 30 m resolution and projected how it shifts through 2100. The headline is not just that flood risk grows, but that it moves, into places current planning and regulatory maps are not watching. The July 2025 Kerrville flooding sat squarely inside the kind of inland hazard expansion this model projects.
KEY TAKEAWAYS:
● 95% of new flood exposure by 2100 is inland, away from the coast, shifting the resilience problem into interior river basins that planning has historically deprioritized.
● Where [they] benchmarked against FEMA's National Flood Hazard Layer, the model flags substantial hidden risk in rapidly urbanizing peri-urban areas, most notably in Greater Houston.
● Climate change alone expands the flood-susceptible footprint by 10–12% by 2100, before any new road or land-use development, so this is a conservative floor, not a ceiling.
● Half the state's roads and rail and 80% of its bridges already sit in flood-susceptible zones today.
● [They] accounted for both factor-importance and spatial-scale uncertainty, using a Monte Carlo weight-perturbation ensemble and multiscale analysis across nested neighborhoods.
The practical takeaway: this is a statewide screening layer, not a replacement for site-level hydraulic studies. It shows planners and policymakers where the gap between today's protection and tomorrow's risk is widest, and where unmapped peri-urban growth is walking into exposure that regulatory maps still call safe…”
#water #hydrology #hydrography #extremeweather #flood #flooding #Texas #TX #USA #transportation #infrastructure #humanimpacts #risk #hazard #cost #economics #floodsusceptibility #GIS #spatial #mapping #raster #elevation #modeling #model #spatialanalysis #planning #regulation #warning #Kerrville #hazardmapping #floodexposure #inland #coast #urban #urbanisation #development #growth #Houston #lowlying #climatechange #landuse #development #geostatstics #MonteCarlo #regionalscreening #naturalhazard #infrastructureresilience #floodmapping #hydrogeomorphology #geomorphometry #aginginfrastructure -
The Growing Threat of Flooding on Transportation Infrastructure Across Texas Through 2100
--
https://doi.org/10.1029/2026EF008207 <--shared paper
--
H/T @Rakibul Ahasan
“[The researchers] modeled flood susceptibility across Texas at 30 m resolution and projected how it shifts through 2100. The headline is not just that flood risk grows, but that it moves, into places current planning and regulatory maps are not watching. The July 2025 Kerrville flooding sat squarely inside the kind of inland hazard expansion this model projects.
KEY TAKEAWAYS:
● 95% of new flood exposure by 2100 is inland, away from the coast, shifting the resilience problem into interior river basins that planning has historically deprioritized.
● Where [they] benchmarked against FEMA's National Flood Hazard Layer, the model flags substantial hidden risk in rapidly urbanizing peri-urban areas, most notably in Greater Houston.
● Climate change alone expands the flood-susceptible footprint by 10–12% by 2100, before any new road or land-use development, so this is a conservative floor, not a ceiling.
● Half the state's roads and rail and 80% of its bridges already sit in flood-susceptible zones today.
● [They] accounted for both factor-importance and spatial-scale uncertainty, using a Monte Carlo weight-perturbation ensemble and multiscale analysis across nested neighborhoods.
The practical takeaway: this is a statewide screening layer, not a replacement for site-level hydraulic studies. It shows planners and policymakers where the gap between today's protection and tomorrow's risk is widest, and where unmapped peri-urban growth is walking into exposure that regulatory maps still call safe…”
#water #hydrology #hydrography #extremeweather #flood #flooding #Texas #TX #USA #transportation #infrastructure #humanimpacts #risk #hazard #cost #economics #floodsusceptibility #GIS #spatial #mapping #raster #elevation #modeling #model #spatialanalysis #planning #regulation #warning #Kerrville #hazardmapping #floodexposure #inland #coast #urban #urbanisation #development #growth #Houston #lowlying #climatechange #landuse #development #geostatstics #MonteCarlo #regionalscreening #naturalhazard #infrastructureresilience #floodmapping #hydrogeomorphology #geomorphometry #aginginfrastructure -
The Growing Threat of Flooding on Transportation Infrastructure Across Texas Through 2100
--
https://doi.org/10.1029/2026EF008207 <--shared paper
--
H/T @Rakibul Ahasan
“[The researchers] modeled flood susceptibility across Texas at 30 m resolution and projected how it shifts through 2100. The headline is not just that flood risk grows, but that it moves, into places current planning and regulatory maps are not watching. The July 2025 Kerrville flooding sat squarely inside the kind of inland hazard expansion this model projects.
KEY TAKEAWAYS:
● 95% of new flood exposure by 2100 is inland, away from the coast, shifting the resilience problem into interior river basins that planning has historically deprioritized.
● Where [they] benchmarked against FEMA's National Flood Hazard Layer, the model flags substantial hidden risk in rapidly urbanizing peri-urban areas, most notably in Greater Houston.
● Climate change alone expands the flood-susceptible footprint by 10–12% by 2100, before any new road or land-use development, so this is a conservative floor, not a ceiling.
● Half the state's roads and rail and 80% of its bridges already sit in flood-susceptible zones today.
● [They] accounted for both factor-importance and spatial-scale uncertainty, using a Monte Carlo weight-perturbation ensemble and multiscale analysis across nested neighborhoods.
The practical takeaway: this is a statewide screening layer, not a replacement for site-level hydraulic studies. It shows planners and policymakers where the gap between today's protection and tomorrow's risk is widest, and where unmapped peri-urban growth is walking into exposure that regulatory maps still call safe…”
#water #hydrology #hydrography #extremeweather #flood #flooding #Texas #TX #USA #transportation #infrastructure #humanimpacts #risk #hazard #cost #economics #floodsusceptibility #GIS #spatial #mapping #raster #elevation #modeling #model #spatialanalysis #planning #regulation #warning #Kerrville #hazardmapping #floodexposure #inland #coast #urban #urbanisation #development #growth #Houston #lowlying #climatechange #landuse #development #geostatstics #MonteCarlo #regionalscreening #naturalhazard #infrastructureresilience #floodmapping #hydrogeomorphology #geomorphometry #aginginfrastructure -
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
--
https://doi.org/10.3390/w18070844 <-- shared paper
--
https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
---
#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence -
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
--
https://doi.org/10.3390/w18070844 <-- shared paper
--
https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
---
#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence -
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
--
https://doi.org/10.3390/w18070844 <-- shared paper
--
https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
---
#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence -
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
--
https://doi.org/10.3390/w18070844 <-- shared paper
--
https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
---
#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence -
Integrated Data-Driven Multi-Criteria Analysis and Machine Learning Approaches for Assessment of Flood Susceptibility Mapping
--
https://doi.org/10.3390/w18070844 <-- shared paper
--
https://youtu.be/N7nyU1cMg5k?si=8WuXIaz4-JKPdCE0 <-- shared video, Mohmand Dam flooding
---
#FloodSusceptibility #FloodMapping #MachineLearning #GIS #RemoteSensing #Hydrology #Water #EnvironmentalResearch #AHP #FAHP #climatechange #extremeweather #GoogleEarthEngine #GIS #spatial #mapping #AI #model #modeling #MohmandDam #SwatRiver #Pakistan #machinelearning #AI #criteria #parameters #indices #rainfall #precipitation #LULC #soiltexture #planning #policy #water #hydrology #hydrography #riskmanagement #risk #hazard #flood #flooding #mitigation #watershed #watermanagement #resilence -
Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
--
https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
--
#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers -
Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
--
https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
--
#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers -
Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
--
https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
--
#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers -
Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
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https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
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#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers -
Flood Susceptibility Mapping Of Kathmandu Metropolitan City Using GIS-Based Multi-Criteria Decision Analysis
--
https://doi.org/10.1016/j.ecolind.2023.110653 <-- shared paper
--
#GIS #spatial #mapping #analyticalhierarchyprocess #floodhazardcriteria #floodpotentialzone #localadministrativeunit #Nepal #spatialanalysis #spatiotemporal #model #modeling #processmodeling #water #hydrology #flood #flooding #Kathmandu #distance #proximity #river #climatechange #extremeweather #precipitation #rainfall #monsoon #builtenvironments #impervious #impervioussurfaces #concrete #stormwater #drainage #economics #environment #infrastructure #damage #ecology #floodinnundation #urbanplanning #criteria #slope #elevation #drainagedensity #rainfall #landuse #landcover #distancefromriver #distance #topography #elevation #floodsusceptibility #floodprone #risk #hazard #mitigation #budgets #costs #policymakers