Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has
further generated immense interest in researching aspects for further improvements to …

Global climate change has led to large fluctuations in lake levels in recent years as
meteorological and hydrological parameters have changed and water use has been …

Rivers carry suspended sediments along with their flow. These sediments deposit at
different places depending on the discharge and course of the river. However, the …

Lake water-level fluctuation is a complex and dynamic process, characterized by high
stochasticity and nonlinearity, and difficult to model and forecast. In recent years …

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies.
This study proposes a novel stochastic model for daily rainfall-runoff simulation called …

Daily water level forecasting is of significant importance for the comprehensive utilization of
water resources. An improved least squares support vector machine (LSSVM) model was …

Owing to the inherent complications in membrane distillation (MD) operations, it has become
a challenge to acknowledge swiftly and appropriately to safeguard the quality of effluent …

This study presents possible applications of machine learning (ML) methods for estimating
water levels without a throughout understanding of hydrological processes and complex …

Globally, the water‐level fluctuations in lakes are a dynamic and complex process. The
fluctuation is characterized by higher non‐linearity and stochasticity, making it quite hard to …

Hydrological models are commonly used to quantify the hydrological impacts of climate
change using general circulation model (GCM) simulations as input. However, application of …