Groundwater Management Based on Time Series and Ensembles of Machine Learning

Abstract

Due to the necessity of effective water management, the issue of water scarcity has developed into a significant global issue. One way to collect water is through the water management method. The most common source of fresh water anywhere in the world is groundwater, which has developed into a significant global issue. Our previous research used machine learning (ML) for training models to classify groundwater quality. However, in this study, we used the time series and ensemble methods to propose a hybrid technique to enhance the multiclassification of groundwater quality. The proposed technique distinguishes between excellent drinking water, good drinking water, poor irrigation water, and very poor irrigation water. In this research, we used the GEOTHERM dataset, and we pre-processed it by replacing the missing and null values, solving the sparsity problem with our recommender system, which was previously proposed, and applying the synthetic minority oversampling technique (SMOTE). Moreover, we used the Pearson correlation coefficient (PCC) feature selection technique to select the relevant attributes. The dataset was divided into a training set (75%) and a testing set (25%). The time-series algorithm was used in the training phase to learn the four ensemble techniques (random forest (RF), gradient boosting, AdaBoost, and bagging. The four ensemble methods were used in the testing phase to validate the proposed hybrid technique. The experimental results showed that the RF algorithm outperformed the common ensemble methods in terms of multiclassification average precision, recall, disc similarity coefficient (DSC), and accuracy for the groundwater dataset by approximately 98%, 89.25%, 93%, and 95%, respectively. As a result, the evaluation of the proposed model revealed that, compared to other recent models, it produces unmatched tuning-based perception results.