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 …

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to
financial constraints in develo** countries, such evaluations suffer from insufficient …

This paper proposes runoff models based on machine learning to estimate daily streamflows
in Oued Sebaou watershed, a Mediterranean coastal basin located in northern Algeria …

Characterized by their high spatiotemporal variability, rainfalls are difficult to predict,
especially under climate change. This study proposes a multilayer perceptron (MLP) …

This research explores machine learning algorithms for reservoir inflow prediction, including
long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models …

The analysis of suspended sediment transport and of its variations over time is crucial for
understanding environmental evolution and it is the key to future challenges caused by …

Predicting rainfall amount is essential in water resources planning and for managing
structures, especially those against floods and long-term drought establishment. Machine …

This study investigates the application of the Gaussian Radial Basis Function Neural
Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron …

Suspended sediment load estimation is vital for the development of river initiatives, water
resources management, the ecological health of rivers, determination of the economic life of …

Current study presents the boosting of two base models, including a Heuristic Search
Algorithm for finding the k shortest paths (K*) and an alternating model tree (AM Tree) …