This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …

Prediction of suspended sediment load (SSL) in streams is significant in hydrological
modeling and water resources engineering. Development of a consistent and accurate …

Lake surface water temperature is one of the most important physical and ecological indices
of lakes, which has frequently been used as the indicator to evaluate the impact of climate …

Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field,
enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of …

In years of increasing impact of climate change effects, a reliable characterization of the
spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant …

Reference Evapotranspiration (ET o) is a complex, dynamic and non-linear hydrological
process. Accurate estimation of ET o has long been an eminent topic of interest in the …

In recent years, the growing impact of climate change on surface water bodies has made the
analysis and forecasting of streamflow rates essential for proper planning and management …

The present study case examined the capability of Artificial Neural Network (ANN), Adaptive
Neuro-Fuzzy Inference System (ANFIS), M5P, and Random Forest (RF) soft-computing …