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Article

Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models

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Irrigation and Water Management Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
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Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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Agricultural Research Centre, Agricultural Engineering Research Institute (AEnRI), Giza 12618, Egypt
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Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, P.O. Box 2454, Riyadh 11451, Saudi Arabia
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Department of Agricultural Engineering, Faculty of Agriculture (El-Shatby), Alexandria University, Alexandria 21545, Egypt
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Faculty V, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
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ASICT Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh
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Discipline of Civil Engineering, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
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Authors to whom correspondence should be addressed.
Academic Editors: Hakan Başağaoğlu, Debaditya Chakraborty and Marcio Giacomoni
Water 2021, 13(21), 3130; https://doi.org/10.3390/w13213130
Received: 15 September 2021 / Revised: 31 October 2021 / Accepted: 2 November 2021 / Published: 6 November 2021
Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs. View Full-Text
Keywords: groundwater level predictions; multiple objective genetic algorithm; evolutionary algorithm optimized ANFIS; ensemble prediction; entropy groundwater level predictions; multiple objective genetic algorithm; evolutionary algorithm optimized ANFIS; ensemble prediction; entropy
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MDPI and ACS Style

Roy, D.K.; Biswas, S.K.; Mattar, M.A.; El-Shafei, A.A.; Murad, K.F.I.; Saha, K.K.; Datta, B.; Dewidar, A.Z. Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models. Water 2021, 13, 3130. https://doi.org/10.3390/w13213130

AMA Style

Roy DK, Biswas SK, Mattar MA, El-Shafei AA, Murad KFI, Saha KK, Datta B, Dewidar AZ. Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models. Water. 2021; 13(21):3130. https://doi.org/10.3390/w13213130

Chicago/Turabian Style

Roy, Dilip Kumar, Sujit Kumar Biswas, Mohamed A. Mattar, Ahmed A. El-Shafei, Khandakar Faisal Ibn Murad, Kowshik Kumar Saha, Bithin Datta, and Ahmed Z. Dewidar. 2021. "Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models" Water 13, no. 21: 3130. https://doi.org/10.3390/w13213130

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