Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models

• Published in: Environmental science and pollution research international Vol. 24; no. 9; pp. 8562 - 8577
• Main Authors: Nadiri, Ata Allah, Gharekhani, Maryam, Khatibi, Rahman, Moghaddam, Asghar Asghari
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017; Springer Nature B.V
• Subjects: Aquatic Pollution; Aquifers; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Conditioning; Contaminants; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Fuzzy Logic; Geology; Groundwater; Hydrogeology; Hydrologic models; Hydrology; Iran; Modelling; Models, Theoretical; Neural networks; Neural Networks (Computer); nitrate nitrogen; Nitrates; Nitrates - analysis; Parameter uncertainty; Research Article; uncertainty; Waste Water Technology; Water Management; Water Pollution - prevention & control; Water Pollution Control; Iran; Fuzzy logic; Vulnerability index; Supervised committee fuzzy logic (SCFL); Ardabil aquifer
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-017-8489-4

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results “conditioned” by nitrate-N values to raise their correlation to higher than 0.9.