The impacts of climate change, especially sea-level rise, are an increasing threat to the
world's coastal regions. Following recommendations made by the United Nations about the …

This study examines whether deep learning models can produce reliable future projections
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …

The alteration in river flow patterns, particularly those that originate in the Himalaya, has
been caused by the increased temperature and rainfall variability brought on by climate …

In the present study, for the first time, a new framework is used by combining metaheuristic
algorithms, decomposition and machine learning for flood frequency analysis under climate …

This paper explores the employment of artificial intelligence and machine learning to
decipher strategic responses to incidences of climate change and to inform the management …

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and
meteorology. This research aimed to examine the capability of machine learning (ML) and …

Accurate streamflow estimation is vital for effective water resources management, including
flood mitigation, drought warning, and reservoir operation. This paper aims to evaluate four …

Previous studies linking large-scale atmospheric circulation and river flow with traditional
machine learning techniques have predominantly explored monthly, seasonal or annual …

Abstract Analysis of rainfall distribution and its changing pattern plays a vital role in
managing water resources in a region. This work examined the spatio-temporal variability …

Real-time streamflow modeling is a challenging endeavor in regions where real-time ground-
based hydro-meteorological observations are scarce. Nevertheless, with the emergence of …