Detection of flood vulnerable areas in urban basins using multi-criteria analysis and geospatial tools: a case study from eastern Mediterranean

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(17)

Published: Aug. 30, 2024

Language: Английский

Flood risk susceptibility analysis in Larkana district Pakistan using multi criteria decision analysis and geospatial techniques

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 20, 2025

Flooding is one of the most devastating natural disasters in worldwide, with significant socioeconomic and environmental impacts. In such a scenario, flood susceptibility analysis essential for successful risk management disaster preparedness. The Larkana district located densely populated area Sindh province, approximately 1.8 million population 0.32 households, highly susceptible to floods due its geographical location, river systems, hot climatic patterns extremely high temperature summer mild winter. often faces risks mainly monsoon rains overflow Indus River which lead widespread flooding. research aims identify quantify key factors, including rainfall pattern, distance river, topography, land use/land cover, soil texture, vegetation index, drainage infrastructure. AHP was applied prioritize these factors based on expert opinions their relative significance contributing vulnerability. GIS employed spatial mapping zones, allowing detailed visualization high-risk areas across district. For this study, various data sources as topographic data, use landcover information, infrastructure were used develop comprehensive model. method determine weights consistency ratio (CR) techniques generate maps by considering all nine factors. Flood levels further classified into five different classes very low, moderate, high. By using AHP, each parameter calculated percentage, it determined that four out parameters had 79% impact hazard. These are ranked influential hazards study area, rainfall, slope, elevation having greatest influences, while LULC, TWI, NDVI, type, curvature 21% impacts, much less compared top parameters. After that, reclassified. superimposed weighted overlay order show 7.65% entire at risk, 63.89% 28.38% moderate 0.12% low risk. findings established complete model will facilitate policymakers authorities identifying locations prioritizing mitigation measures, ultimately reducing local communities

Language: Английский

Flash flood susceptibility modeling using optimized deep learning method in the Uttarakhand Himalayas

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 11, 2024

Language: Английский