Using a Mamdani Fuzzy Inference System Model (MFISM) for Ranking Groundwater Quality in an Agri-Environmental Context: Case of the Hammamet-Nabeul Shallow Aquifer (Tunisia)

Using an adaptive Mamdani fuzzy inference system model (MFSIM), the purpose of this paper is mainly to assess and rank assessment ranking water quality for irrigation occurring in Hammamet-Nabeul (Tunisia) shallow aquifer. This aquifer under Mediterranean climate conditions affected by intensive irrational agricultural activities. In current study, logic-based decision-making approach was adapted classify groundwater (GW) irrigation. The operation based on input membership functions electrical conductivity (EC) sodium absorption ratio (SAR) output function index (IWQI). Validation applied MFISM showed a rate about 80%. Therefore, shown be reliable flexible uncertain complex hydrogeological system. results demonstrated that contamination overlaying three types negative anthropogenic practices: excess use chemical fertilizers, rejection partially treated wastewater some areas. implemented led identifying spatial distribution studied area. It considered helpful tool agri-environmental sustainability management.