Anomaly detection in geostatistical models with application to groundwater level data in the Gaza Coastal Aquifer

In geostatistics, the detection of anomalous observations has a particular importance because of the changes they can create in environmental and geological patterns. Few methods for detecting such observations in univariate data have been proposed for the spatial case, namely sample influence function (SIF), kriging, Intrinsic Random Functions (IRF), and geostatistical functional data. This article reviews the main outlier detection procedures in the context of geostatistics, and due to the absence of a numerical comparison between them, this article obtained the cut-off points of these methods for three different variogram models, and evaluated their performance via a simulation study. The results show that for all detection methods and the three considered models, there is an inverse relationship between the level of contamination and power of performance. In addition, the SIF for the cubic variogram model outperforms the exponential and Matérn. Because of the peculiarities of the Gaza Strip, as regards Palestine water condition, and for illustration purposes, we consider real groundwater level data in the Gaza Coastal Aquifer, where a set of possible outliers were identified

Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques

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.