Cited by Mapping flood risk using a workflow including deep learning and MCDM

Spatial analysis of flood susceptibility in Coastal area of Pakistan using machine learning models and SAR imagery DOI

Muhammad Afaq Hussain,

Zhanlong Chen,

Yulong Zhou

и другие.

Environmental Earth Sciences, Год журнала: 2025, Номер 84(5)

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

Multi-criteria Assessment of Potential Check Dam Location for Sustainable Development in Urban River Basins of the Eastern Mediterranean DOI

Hazem Ghassan Abdo, Sahar Mohammed Richi,

Mohammed J. Alshayeb

и другие.

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin DOI

Chiranjit Singha, Satiprasad Sahoo,

Alireza Bahrami Mahtaj

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 124972 - 124972

Опубликована: Март 23, 2025

The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic environmental stability. This study presents a novel approach susceptibility (FS) mapping updates the region's inventory. Multitemporal Sentinel-1 (S1) SAR images (2020-2022) were processed using U-Net transfer learning model generate water body frequency map, which was integrated with Global Flood Dataset (2000-2018) refined through grid-based classification create an updated Eleven geospatial layers, including elevation, slope, soil moisture, precipitation, type, NDVI, Land Use Cover (LULC), wind speed, drainage density, runoff, used as conditioning factors (FCFs) develop hybrid FS approach. integrates Fuzzy Analytic Hierarchy Process (FuzzyAHP) six machine (ML) algorithms models FuzzyAHP-RF, FuzzyAHP-XGB, FuzzyAHP-GBM, FuzzyAHP-avNNet, FuzzyAHP-AdaBoost, FuzzyAHP-PLS. Future trends (1990-2030) projected CMIP6 data under SSP2-4.5 SSP5-8.5 scenarios MIROC6 EC-Earth3 ensembles. SHAP algorithm identified LULC, type most influential FCFs, contributing over 60 % susceptibility. Results show that 31.10 of basin is highly susceptible flooding, western regions at greatest risk low elevation high density. projections indicate 30.69 area will remain vulnerable, slight increase SSP5-8.5. Among models, FuzzyAHP-XGB achieved highest accuracy (AUC = 0.970), outperforming FuzzyAHP-GBM 0.968) FuzzyAHP-RF 0.965). experimental results showed proposed can provide spatially well-distributed inventory derived from freely available remote sensing (RS) datasets robust framework for long-term assessment ML techniques. These findings offer critical insights improving management mitigation strategies basin.

Язык: Английский

Процитировано

Landslide-induced vulnerability of road networks in Lahaul and Spiti, India: a geospatial study DOI

Devraj Dhakal, Kanwarpreet Singh, Damandeep Kaur

и другие.

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(6)

Опубликована: Май 24, 2025

Язык: Английский

Процитировано

A real-time prediction model for instantaneous dam-break flood evolution of concrete gravity dams based on attention mechanism and spatiotemporal multiple features DOI

Chao Wang,

Yaofei Zhang,

Sherong Zhang

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110616 - 110616

Опубликована: Март 23, 2025

Язык: Английский

Процитировано

Mapping flood risk using a workflow including deep learning and MCDM– Application to southern Iran DOI

Hamid Gholami,

Aliakbar Mohammadifar,

Shahram Golzari

и другие.

Urban Climate, Год журнала: 2024, Номер 59, С. 102272 - 102272

Опубликована: Дек. 27, 2024

Язык: Английский

Процитировано