Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …

Disaster management is a critical area that requires efficient methods and techniques to
address various challenges. This comprehensive assessment offers an in-depth overview of …

This study investigates the effectiveness of three deep learning methods, Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …

Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

Accurate wind power prediction is critical for efficient grid management and the integration of
renewable energy sources into the power grid. This study presents an effective deep …

Abstract Long Short-Term Memory (LSTM) has recently emerged as a crucial tool for
scientific research in hydrology and water resources. Despite its widespread use, a …

Rainfall-runoff modeling is a complex nonlinear spatiotemporal prediction problem.
However, few studies have considered the spatial characteristics of rainfall-runoff …

Recent advancements in artificial intelligence (AI) technology have offered new ways to
obtain the relative permeability curve that is crucial for subsurface engineering problems …

This study investigates the feasibility of using hybrid models namely Convolutional Neural
Network (CNN)-Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) …

Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …