Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques. Issue 3 (2nd March 2023) - Aish, Adnan M.; Zaqoot, Hossam Adel; Sethar, Waqar Ahmed; Aish, Diana A.
Abstract: This paper investigates the performance of five supervised machine learning algorithms, including support vector machine (SVM), logistic regression (LogR), decision tree (DT), multiple perceptron neural network (MLP-NN), and K-nearest neighbours (KNN) for predicting the water quality index (WQI) and water quality class (WQC) in the coastal aquifer of the Gaza Strip. A total of 2, 448 samples of groundwater were collected from the coastal aquifer of the Gaza Strip, and various physical and chemical parameters were measured to calculate the WQI based on weight. The prediction accuracy was evaluated using five error measures. The results showed that MLP-NN outperformed other models in terms of accuracy with an R value of 0.9945–0.9948, compared with 0.9897–0.9880 for SVM, 0.9784–0.9800 for LogR, 0.9464–0.9247 for KNN, and 0.9301–0.9064 for DT. SVM classification showed that 78.32% of the study area fell under poor to unsuitable water categories, while the north part of the region had good to excellent water quality. Total dissolved solids (TDS) was the most important parameter in WQI predictions while and were the least important. MLP-NN and SVM were the most accurate models for the WQI prediction and classification in the Gaza coastal aquifer. HIGHLIGHTS: Machine learning (ML) algorithms are used for predicting water quality index. Prediction performance of LogR, DT, KNN, SVM, and MLP-NN are compared. MLP-NN and SVM-based prediction and quality classification models performed better than other ML-developed models. Gaza coastal aquifer is experiencing a severe deterioration in water quality, as it is currently unsafe for drinking purposes without adequate treatment. Graphical Abstract