Abdulaziz Alqahtani

TL;DR: A framework for tuning the hyper-parameters of feed forward neural network and gene expression programming with particle swarm optimization with PSO is proposed, which demonstrated that the implementation of artificial intelligence models with optimization routine can lead to optimized models for accurate prediction of water quality.

TL;DR: In this article, the authors investigated the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations.

TL;DR: In this paper , a comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan.

TL;DR: In this paper, an analytical model relying on superposition of the Theis equation is used to resolve water levels at 40 wells in three vertically stacked aquifer storage and recovery (ASR) wellfields operating in the Denver Basin Aquifers, Colorado.

TL;DR: In a world that is ever more focused on energy efficiency and climate change mitigation, minimizing energy consumption associated with pumping groundwater is a growing concern as mentioned in this paper. But, as discussed in this stud...