Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calcul...

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Autores principales:	Saber Kouadri, Ahmed Elbeltagi, Abu Reza Md. Towfiqul Islam, Samir Kateb
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Artificial intelligence Water quality index Modelling Sensitivity analysis Random forest Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/899f15adc69d42de8663ca7c9dfb3cb3
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spelling	oai:doaj.org-article:899f15adc69d42de8663ca7c9dfb3cb32021-11-07T12:22:34ZPerformance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)10.1007/s13201-021-01528-92190-54872190-5495https://doaj.org/article/899f15adc69d42de8663ca7c9dfb3cb32021-11-01T00:00:00Zhttps://doi.org/10.1007/s13201-021-01528-9https://doaj.org/toc/2190-5487https://doaj.org/toc/2190-5495Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.457210–08, 2.141810–08, 1.257310–10%, and 3.170810–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.Saber KouadriAhmed ElbeltagiAbu Reza Md. Towfiqul IslamSamir KatebSpringerOpenarticleArtificial intelligenceWater quality indexModellingSensitivity analysisRandom forestWater supply for domestic and industrial purposesTD201-500ENApplied Water Science, Vol 11, Iss 12, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Water quality index Modelling Sensitivity analysis Random forest Water supply for domestic and industrial purposes TD201-500
spellingShingle	Artificial intelligence Water quality index Modelling Sensitivity analysis Random forest Water supply for domestic and industrial purposes TD201-500 Saber Kouadri Ahmed Elbeltagi Abu Reza Md. Towfiqul Islam Samir Kateb Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
description	Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.457210–08, 2.141810–08, 1.257310–10%, and 3.170810–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.
format	article
author	Saber Kouadri Ahmed Elbeltagi Abu Reza Md. Towfiqul Islam Samir Kateb
author_facet	Saber Kouadri Ahmed Elbeltagi Abu Reza Md. Towfiqul Islam Samir Kateb
author_sort	Saber Kouadri
title	Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
title_short	Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
title_full	Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
title_fullStr	Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
title_full_unstemmed	Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
title_sort	performance of machine learning methods in predicting water quality index based on irregular data set: application on illizi region (algerian southeast)
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/899f15adc69d42de8663ca7c9dfb3cb3
work_keys_str_mv	AT saberkouadri performanceofmachinelearningmethodsinpredictingwaterqualityindexbasedonirregulardatasetapplicationonilliziregionalgeriansoutheast AT ahmedelbeltagi performanceofmachinelearningmethodsinpredictingwaterqualityindexbasedonirregulardatasetapplicationonilliziregionalgeriansoutheast AT aburezamdtowfiqulislam performanceofmachinelearningmethodsinpredictingwaterqualityindexbasedonirregulardatasetapplicationonilliziregionalgeriansoutheast AT samirkateb performanceofmachinelearningmethodsinpredictingwaterqualityindexbasedonirregulardatasetapplicationonilliziregionalgeriansoutheast
_version_	1718443500431736832

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

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