Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …

Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …

The ability of Bayesian networks (BNs) to model complex systems and uncertainties makes it
a perfect tool for the research on subfields related to climate change. In fact, in the past 30 …

Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …

River flow forecasting is important for flood prediction and effective utilization of water
resources. This study proposed a comprehensive methodology that simultaneously enables …

Abstract Model parameters estimation is a pivotal issue for runoff modeling in ungauged
catchments. The nonlinear relationship between model parameters and catchment …

This study aims to assess different machine learning approaches for streamflow
regionalization in a tropical watershed, analyzing their advantages and limitations, and to …

Accurate monitoring of water quality is of great importance, especially in arid and semi-arid
countries such as Iran. The Total Dissolved Solids (TDS) plays quite a significant role in …

The need for adequate data on the spatial and temporal variability of freshwater resources is
a significant challenge to the water managers of the world in water resource planning and …

In recent times, the densely populated Bengaluru metropolis in India has faced challenges
related to water scarcity, particularly relying on the Krishna Raja Sagara (KRS) dam. The …