Abstract The application of Artificial Neural Networks (ANNs) in the field of environmental
and water resources modelling has become increasingly popular since early 1990s. Despite …

Since the middle of the twentieth century, artificial intelligence (AI) models have been used
widely in engineering and science problems. Water resource variable modeling and …

Streamflow forecasting, especially the long lead-time forecasting, is still a very challenging
task in hydrologic modeling. This could be due to the fact that the forecast accuracy …

To cope with the nonlinear and nonstationarity challenges faced by conventional runoff
forecasting models and improve daily runoff prediction accuracy, a hybrid model-based …

Due to increasingly serious deterioration of surface water quality, effective water quality
prediction technique for real-time early warning is essential to guarantee the emergency …

Abstract The Artificial Neural Network (ANN) is a powerful data-driven model that can
capture and represent both linear and non-linear relationships between input and output …

In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN)
based downscaling for mean monthly precipitation and temperature of two stations (Tabriz …

Real-time and short-term prediction of river flow is essential for efficient flood management.
To obtain accurate flow predictions, a reliable rainfall-runoff model must be used. This study …

Investigating terrestrial water cycle dynamics is vital for understanding the recent climatic
variability and human impacts in the hydrologic cycle. In this study, a downscaling approach …

High-quality precipitation data input and the selection of reasonable and applicable
hydrological models are the main ways to improve the accuracy of runoff simulation, and are …