GROUNDWATER QUALITY FORECASTING USING MACHINE LEARNING ALGORITHMS: CASE STUDY BERRECHID AQUIFER, CENTRAL MOROCCO

Abstract

In order to provide a recommendation on the quality of groundwater in the region of Berrechid, Morocco, we analysed the concentration of conductivity as one of the main measures to identify the salinity of the water. We applied artificial intelligence models for predicting the conductivity of water while analysing several physical parameters as input parameters of the models. To achieve this purpose, we exploited and evaluated the Random Forest (RF), Support Vector Regression (SVR), and k-nearest neighbour models using 400 data samples related to ten groundwater quality parameters in the Berrechid aquifer, Morocco. The results revealed that the overall prediction performance of the RF models is higher than the SVR and KNN models. Overall, the developed models are able to predict conductivity with high accuracy. The approaches developed in this study are promising for real-time and low-cost prediction of groundwater quality by using physical parameters as input variables.