Performance of artificial intelligence model (LSTM model) for estimating and predicting water quality index for irrigation purposes in order to improve agricultural production.

The primary goal of this study is to predict the current and future water quality index for irrigation (WQII) of the western Mitidja alluvial aquifer in northern Algeria. The modified WQII was used to evaluate groundwater suitability for irrigation through geographic information system (GIS) techniques. Additionally, a long short-term memory (LSTM) model was employed to calculate the WQII and map future groundwater quality, considering factors like overexploitation, anthropogenic pollution, and climate change.

Hydrogeochemical and isotopic assessment for characterizing groundwater quality in the Mitidja plain (northern Algeria).

Coastal aquifer overexploitation represents a concerning trigger for water salinization around the world and especially in arid and semi-arid regions along with urban growth and urbanization, as well as land use human-induced changes. This study aims to assess the groundwater quality in the Mitidja alluvial aquifer (northern Algeria) along with its suitability for domestic and agricultural utilizations. A hydrogeochemical approach, based on the interpretation of groundwater physiochemical parameters (EC, pH, dry residue, Ca, Mg, Na, K, Cl, SO, HCO, and NO) collected during the wet and dry periods for the years 2005 and 2017 along with an isotopic characterization, including stable isotopes to identify the recharge sources for the samples collected in October 2017, has been proposed.