Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran

Groundwater is considered as the most important natural resources in arid and semi-arid regions. In this study, the application of random forest (RF) and maximum entropy (ME) models for groundwater potential mapping is investigated at Mehran Region, Iran. Although the RF and ME models have been applied widely to environmental and ecological modeling, their applicability to other kinds of predictive modeling such as groundwater potential mapping has not yet been investigated. About 163 groundwater data with high potential yield values of ≥11m3/h were obtained from Iranian Department of Water Resources Management (IDWRM). Further, these selected wells were randomly divided into a dataset 70% (114 wells) for training and the remaining 30% (49 wells) was applied for validation purposes. In total, ten groundwater conditioning factors that affect the storage of groundwater occurrences (e.g. altitude, slope percent, slope aspect, plan curvature, drainage density, distance from rivers, topographic wetness index (TWI), landuse, lithology, and soil texture) were used as input to the models. Subsequently, the RF and ME models were applied to generate the groundwater potential maps (GPMs). Moreover, a sensitivity analysis was used to identify the impact of variable uncertainties on the produced GPMs. Finally, the results of the GPMs were quantitatively validated using observed groundwater dataset and the receiver operating characteristic (ROC) method. Area under ROC curve (AUC) was used to compare the performance of RF with ME. The uncertainty on the preparation of conditioning factors was taken in account to enhance the model. The validation results showed that the AUC for success rate of RF and ME models was 86.5 and 91%, respectively. In contrast, the AUC for prediction rate of RF and ME methods was obtained 83.1 and 87.7%, respectively. Therefore, RF and ME were found to be effective models for groundwater potential mapping.