Integrating CMIP6 climate change projections into machine learning–based flood susceptibility mapping: a comparative analysis for Pontian district, Johor

Multi-Temporal Satellite Observations and Machine Learning-Based Flood Susceptibility Assessment of the 2025 Punjab Flood

Over the past decade, the Punjab plains of Northern India have experienced recurrent flooding driven by hydroclimatic variability, specifically shifts in western disturbances that have intensified monsoon precipitation. Following a devastating flood in 2025, the region remains highly vulnerable to hydrological extremes...

Machine Learning-Informed Hydrological Response Time Modeling in Tropical Watersheds

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and wa...

Integrating hydrologic response into climate change model evaluation and machine learning-based multi-model ensemble approach for extreme climate indices and spatio-temporal projections

National-Scale Flood Susceptibility Mapping of Nigeria Using Statistical and Machine Learning Models with Satellite-Driven Validation for Data-Sparse Environments

Flooding is the most recurrent and economically devastating natural hazard in Nigeria, yet no standard or consistent nationwide assessment method exists. Spatially explicit flood susceptibility information also remains scarce, particularly given limited hydrometric monitoring. This study comparatively evaluates nationa...

Future Streamflow Projections in a Semi-Arid Mountain Basin Using Machine Learning and CMIP6 Climate Scenarios: The Case of the Zat River (Morocco)

Understanding how climate change may alter river discharge in semi-arid regions is essential for sustainable water-resource management. This study assesses future streamflow in the Zat River Basin (High Atlas Mountains, Morocco) using a hybrid framework that combines machine-learning rainfall–runoff modeling, CMIP6 mul...

Integrating remote sensing and machine learning for flood hazard zonation in Gomati district of Tripura, Northeast India

Floods represent a major hazard in India’s sub-Himalayan regions, with recurrent events in Tripura causing significant socio-economic and infrastructural losses. The August 2024 flood in Gomati district highlighted the urgent need for accurate flood susceptibility mapping to guide risk mitigation and disaster preparedn...