ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area
| Autor: | Chidambaram Sabarathinam, Mohamed Hassan, Venkatramanan Senapathi, Sang Yong Chung, Kye-Hun Park, Hussam Eldin Elzain, Selvam Sekar |
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| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
0208 environmental biotechnology Soil science Aquifer 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Hydraulic conductivity Vadose zone Republic of Korea Waste Management and Disposal Groundwater 0105 earth and related environmental sciences Adaptive neuro fuzzy inference system geography geography.geographical_feature_category Hydrogeology Nitrates General Medicine Groundwater recharge Contamination Models Theoretical 020801 environmental engineering Environmental science Nitrogen Oxides Environmental Monitoring |
| Zdroj: | Journal of environmental management. 286 |
| ISSN: | 1095-8630 |
| Popis: | The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular models. ANFIS-MOA models were also useful for resolving the subjectivity of physical and hydrogeological parameters in original DRASTIC method (ODM) and MDM. It is expected that ANFIS-PSO models will produce the excellent results in assessing groundwater contamination vulnerability and that they can greatly contribute to the groundwater security in other areas of the world as well as Miryang City of South Korea. |
| Databáze: | OpenAIRE |
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