Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Selecting an adequate set of inputs is a critical step for successful data-driven streamflow
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …

Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial
patterns, which cause great difficulty in quantifying their complex processes, while reliable …

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

Although numerical and non-numerical models of groundwater flow and transport have
separately been reviewed in several studies, they have not hitherto been reviewed …

In this study, five different artificial intelligence methods, including Artificial Neural Networks
based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi …

Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable
of optimizing water resource systems or planning future expansion. This study investigated …

Daily groundwater level is an indicator of groundwater resources. Accurate and reliable
groundwater level (GWL) prediction is crucial for groundwater resources management and …

The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models
are proposed for forecasting highly non-linear streamflow of Pahang River, located in a …