Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends

• Main Authors: Hanoon M.S., Ahmed A.N., Fai C.M., Birima A.H., Razzaq A., Sherif M., Sefelnasr A., El-Shafie A.
• Other Authors: 57266877500
• Format: Review
• Published: Springer Science and Business Media Deutschland GmbH 2023

Aquifers; Forecasting; Groundwater resources; Hydrogeology; Machine learning; ANN; Artificial intelligence; Artificial intelligence methods; Future trends; Groundwater quality; Hybrid model; Intelligence models; Machine learning; Quality modeling; Water quality; boron; chloride; fluoride; ground water; nitrate; phosphate; sulfate; zinc; accuracy assessment; aquifer; artificial intelligence; future prospect; groundwater; guideline; machine learning; prediction; reliability analysis; trend analysis; water quality; accuracy; alkalinity; artificial intelligence; artificial neural network; chemical oxygen demand; concentration (parameter); electric conductivity; Escherichia coli; feed forward neural network; fuzzy logic; human; machine learning; multilayer perceptron; nonhuman; pH; physical parameters; practice guideline; prediction; radial basis function; radial basis function neural network; Review; single layer perceptron; support vector machine; suspended particulate matter; total dissolved solid; total hardness; trend study; turbidity; water quality