Predicting Thermal Structure in Large Reservoirs Under Climate Change Using Machine Learning

Comparative Evaluation of Physics-Informed and Data-Driven Neural Networks for Reservoir Water Balance Simulation During Hydrological Extremes

New classes of climate model emulators to improve paleoclimate reconstructions

A hierarchy of CMIP-class climate model emulators, for annual surface air temperature field emulation, that integrate autoencoder-based dimensionality reduction with nonlinear prediction architectures, including Reservoir Computing and Recurrent Neural Networks are developed and evaluated.

Environmental Modeling and Assessment Using CFD and Data-Driven Tools

An integrated set of methodologies that applies both three-dimensional computational fluid dynamics (CFD) and cutting-edge machine learning (ML) and deep learning (DL) methodologies for the environmental thermal assessment is provided.

Physical and data hybrid-driven modeling for the evolution state prediction of reservoir colluvial landslides with step-like movement patterns: a case study in the Xiluodu Reservoir area, China

Deep Learning Based Climate Forecasting Model Using LSTM (Long Short-Term Memory) of Kathmandu Valley

Deep learning’s significant potential as a scalable and dependable method for regional climate forecasting, giving it an advantage in the Kathmandu Valley’s climate change adaptation policy-making process is supported.

A Driver‐Aware Machine Learning Approach for Predicting the Severity of Decadal‐Scale Extreme Climate Events

An integrated framework providing a way to incorporate information on large‐scale climate drivers into data‐driven prediction models is proposed and shown to incorporate information on large‐scale climate drivers into data‐driven prediction models.