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Open access Sep 2026

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

A balanced interaction between extreme rainfall, runoff potential, and topographic control in causing periodic floods in the Punjab plains reflects a balanced interaction between extreme rainfall, runoff potential, and topographic control.

Ankush Kumar, Ashwani Raju, Saraah Imran et al. · 0 citations
Open access 2026

SAR-Based Flood and Waterlogging Extent Mapping Using Sentinel – 1 Time Series Backscatter Analysis: A Case Study of the FCT

Flooding is still one of the most important environmental dangers for rapidly urbanising communities, especially when the prompt flood monitoring is hindered by the continuous cloud cover and shortage of hydrological measurements. This study established a multi-temporal flood mapping methodology using Sentinel-1 Synthe...

Ochimana Ebiojo · 0 citations
Open access Sep 2026

Multi-Temporal Assessment of Bimodal Monsoon Flood Dynamics and Agricultural Exposure Using Integrated Sentinel-1 SAR and Sentinel-2 Optical Data in Punjab, Pakistan

Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud c...

Nida Khursheed, Asif Sajjad, Mazhar Iqbal et al. · 0 citations
Conference Aug 2026

AI-Powered Urban Resilience a Dynamic System for Flood Prediction and Active Management

As climate change, rapid urbanization, and inadequate drainage infrastructure continue to increase the frequency and severity of urban flooding, intelligent and reliable flood prediction systems have become essential for minimizing disaster impacts and improving urban resilience. This study presents a novel Proposed Hy...

R. J., L. D, M. V et al. · 0 citations
Open access Aug 2026

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

The integrated methodology demonstrates the potential of combining SAR data and ML techniques for reliable flood susceptibility assessment, providing a replicable framework for other flood-prone regions.

Sah Kausar Reza, J. Chakraborty, S. Chattaraj et al. · 1 citation

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