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

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 o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dilip Kumar Roy, Sujit Kumar Biswas, Mohamed A. Mattar, Ahmed A. El-Shafei, Khandakar Faisal Ibn Murad, Kowshik Kumar Saha, Bithin Datta, Ahmed Z. Dewidar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	groundwater level predictions multiple objective genetic algorithm evolutionary algorithm optimized ANFIS ensemble prediction entropy Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/ad7be52a1d7944f7890b6f21f46aac1e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ad7be52a1d7944f7890b6f21f46aac1e
record_format	dspace
spelling	oai:doaj.org-article:ad7be52a1d7944f7890b6f21f46aac1e2021-11-11T19:58:17ZGroundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models10.3390/w132131302073-4441https://doaj.org/article/ad7be52a1d7944f7890b6f21f46aac1e2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3130https://doaj.org/toc/2073-4441Predicting 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.Dilip Kumar RoySujit Kumar BiswasMohamed A. MattarAhmed A. El-ShafeiKhandakar Faisal Ibn MuradKowshik Kumar SahaBithin DattaAhmed Z. DewidarMDPI AGarticlegroundwater level predictionsmultiple objective genetic algorithmevolutionary algorithm optimized ANFISensemble predictionentropyHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3130, p 3130 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	groundwater level predictions multiple objective genetic algorithm evolutionary algorithm optimized ANFIS ensemble prediction entropy Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	groundwater level predictions multiple objective genetic algorithm evolutionary algorithm optimized ANFIS ensemble prediction entropy Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Dilip Kumar Roy Sujit Kumar Biswas Mohamed A. Mattar Ahmed A. El-Shafei Khandakar Faisal Ibn Murad Kowshik Kumar Saha Bithin Datta Ahmed Z. Dewidar Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
description	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.
format	article
author	Dilip Kumar Roy Sujit Kumar Biswas Mohamed A. Mattar Ahmed A. El-Shafei Khandakar Faisal Ibn Murad Kowshik Kumar Saha Bithin Datta Ahmed Z. Dewidar
author_facet	Dilip Kumar Roy Sujit Kumar Biswas Mohamed A. Mattar Ahmed A. El-Shafei Khandakar Faisal Ibn Murad Kowshik Kumar Saha Bithin Datta Ahmed Z. Dewidar
author_sort	Dilip Kumar Roy
title	Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
title_short	Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
title_full	Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
title_fullStr	Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
title_full_unstemmed	Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
title_sort	groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of anfis models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ad7be52a1d7944f7890b6f21f46aac1e
work_keys_str_mv	AT dilipkumarroy groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT sujitkumarbiswas groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT mohamedamattar groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT ahmedaelshafei groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT khandakarfaisalibnmurad groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT kowshikkumarsaha groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT bithindatta groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels AT ahmedzdewidar groundwaterlevelpredictionusingamultipleobjectivegeneticalgorithmgreyrelationalanalysisbasedweightedensembleofanfismodels
_version_	1718431369065922560

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

Ejemplares similares