GIS-BASED GROUNDWATER POTENTIAL MAPPING USING MACHINE LEARNING MODELS, A CASE STUDY: QOM PROVINCE, IRAN
Keywords: Groundwater Potential Mapping, Geographic Information Systems, GIS, Random Forest, SVR, Iran
Abstract. By considering the increasing trend in water consumption and significant reduction of water resources in most countries of the world, groundwater resources have become very important. The Target of this study is to implement machine learning models to produce a groundwater potential map (GWPM), identify areas with higher water potential, and also identify influencing factors. Therefore, two algorithms including the random forest (RF) and support vector regression (SVR), were performed that according to the literature have a good compatibility with this type of problems, compared to the other models. Of the 351 well points available throughout the study area, 70% (245 well points) were selected as the target for training the models and the rest 30% (106 well points) were used for evaluating the models. In addition, 20 effective information layers were used for modeling. In this study, an effort was made to focus more on data preparation that is one of the most important parts of model development. The variance inflation factor (VIF) and correlation coefficient were applied to identify the dependent variables. Also, feature selection was done to identify the most influential factors. Finally, two groundwater potential map(GWPM)s were created based on these two models. By calculating the area under the curve (AUC) from the receiver operating characteristic (ROC), the prediction accuracy of the two models was calculated. The values for AUC of the two maps produced by the RF and SVR algorithms were 93.4% and 89.7%, respectively. This study improves the knowledge of groundwater potential in the study area which is one of the cities with water scarcity in the country.