Assessment of groundwater vulnerability using genetic algorithm and random forest methods (case study: Miandoab plain, NW of Iran)

One of the appropriate ways to prevent groundwater contamination is identifying the vulnerable areas of the aquifers. The DRASTIC framework, for assessing the intrinsic vulnerability of the aquifer, is a common method which uses a specific parameter’s weight and a uniform distributed contaminant in overall the aquifer. Therefore, it should be calibrated for specific aquifer and contaminant distribution conditions. In this research, random forest (RF) and genetic algorithm (GA) methods were used for DRASTIC framework optimization in Miandoab plain (NW of Iran). In optimizing the basic DRASTIC framework (BDF) using GA, decision variables are the weight of DRASTIC parameters and weight values for each data layer are the outputs of the optimization. The final optimized map (BDF-GA map) was obtained using overlaying the layers with optimized weights based on the GA method. In optimization of BDF using RF, the model is made up of from 1 to 100 trees and the m parameter or split variables was optimized by changing the number of variables between one and the maximum variables of each subset. Also, the feature selection method is used to reduce the dimensions and increase the accuracy of the model. To induct the nitrate contaminant model, raster layer data of 7 BDF parameters, together with the target variable (VI of BDF map), were used. In the final step, variables’ importance was identified by the RF method and then, the vulnerability map was obtained based on variable importance. In validation and comparison of methods with CI and ROC methods, the BDF-RF method with the higher CI and ROC values was ranked as the most accurate approach in groundwater vulnerability evaluation. The optimized map using the RF method (BDF-RF map) showed that 14.5, 13, 18, 26.5, and 28% of the plain are located in areas with very low, low, moderate, high, and very high vulnerability categories, respectively.