Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria.

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data.

Assessment of groundwater vulnerability using remote sensing, susceptibility index, and WetSpass model in an arid region (Biskra, SE Algeria).

Biskra region currently shows signs of stress and a high risk of groundwater contamination by various chemicals and pesticides. For this purpose, a modified integrated susceptibility index (SI) is coupled with remote sensing (RS) and WetSpass model to assess the sensitivity of the groundwater and the risk of pollution in the most exploited aquifer (Quaternary aquifer) in the study area. The results of the modified SI model show that a major part of the aquifer is at risk of contamination if the farmers do not implement good agricultural practices.