A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability. Issue 6 (23rd January 2018)

• Record Type: Journal Article
• Authors: Agoubi, Belgacem; Dabbaghi, Radhia; Kharroubi, Adel
• Is Part Of: Ground water. Volume 56:Issue 6(2018)
• Page Start: 978
• Page End: 985
• Publication Date: 2018-01-23
• Subjects: Groundwater -- Periodicals; Wells -- Periodicals; Eau souterraine -- Périodiques; Puits -- Périodiques; Grondwater; Eau souterraine; Puits; Electronic journals; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 551.49
• DOI: 10.1111/gwat.12634
• Languages: English
• ISSNs: 0017-467X

Abstract

Abstract: Assessing groundwater vulnerability is an important procedure for sustainable water management. Various methods have been developed for effective assessment of groundwater vulnerability and protection. However, each method has its own conditions of use and, in practice; it is difficult to return the same results for the same site. The research conceptualized and developed an improved DRASTIC method using Mamdani Adaptive Neural Fuzzy Inference System (M‐ANFIS‐DRASTIC). DRASTIC and M‐ANFIS‐DRASTIC were applied in the Jorf aquifer, southeastern Tunisia, and results were compared. Results confirm that M‐ANFIS‐DRASTIC combined with geostatistical tools is more powerful, generated more precise vulnerability classes with very low estimation variance. Fuzzy logic has a power to produce more realistic aquifer vulnerability assessments and introduces new ways of modeling in hydrogeology using natural human language expressed by logic rules. Abstract : Article impact statement : Improvement of vulnerability DRASTIC using Fuzzy Logic M‐ANFIS.