Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme
weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In …

Clouds and haze often occlude optical satellite images, hindering continuous, dense
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …

Geospatial resources, including satellite-based synthetic aperture radar (SAR) and optical
data, have been instrumental in providing time-sensitive information about the extent and …

This paper focusses on the optimal implementation of a Mean Variance Estimation network
(MVE network)(Nix and Weigend, 1994). This type of network is often used as a building …

Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in
which concurrent or successive flood drivers synergize, producing larger impacts than those …

Abstract The Lower Kosi Basin (LKB) in North Bihar is highly prone to floods and is
influenced by upstream hydrology. A flood susceptibility index has been modelled by …

Robust and accurate flood hazard maps are essential for early warning systems and flood
risk management. Although physically based models are effective in estimating pluvial …

Regression and classification serve as indispensable machine learning (ML) tasks in urban
flood hazard mapping. While a machine learning classifier can effectively predict flood …

As a frequent and devastating natural disaster worldwide, floods are influenced by complex
factors. Building flood models for simulating, monitoring, and forecasting floods is crucial to …