Artificial intelligence modelling framework for mapping groundwater vulnerability of fractured aquifer
Mohamed Haythem Msaddek, Yahya Moumni, Alaeddine Ayari, Moufida El May, Ismail Chenini
Abstract
In this work, a new parameter of fracture media is introduced to the conventional DRASTIC model to establish a new model for the fractured aquifer vulnerability mapping. Therefore, Artificial Intelligence technique (AI) is employed to reduce potential uncertainties using unsupervised Multi-Frameworks Technique (MFT) (Standard DRASTIC-Fr Framework (SDF) and Fuzzy Membership Framework (FMF)) and supervised learning based on Multi-Models Approach (MMA) (Genetic Algorithm (GA) and Genetic Expression Programming (GEP)). This novel framework comprises two stages; Stage 1 building of two frameworks MFT and models MMA; Stage 2 developing four frameworks including MFT/MMA combination based on Support Vector Machine algorithm. The proposed framework is tested with the fractured aquifer of the Meknassy area (central Tunisia), where the rapid expansive agricultural habits have been converted into excessive usage of fertilizers. Outputs demonstrate that the vulnerability in the study area is very low VL = 31%, low L = 21.5%, moderate M = 22.9%, high H = 15% and very high VH = 9.6%.