Dissolved oxygen modelling of the Yamuna River using different ANFIS models

Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are be...

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Autores principales:	Sameer Arora, Ashok K. Keshari
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	dissolved oxygen grid partitioning subtractive clustering water quality yamuna river Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/21b1a106fe9f4b1e82dcab7f0f16dafa
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spelling	oai:doaj.org-article:21b1a106fe9f4b1e82dcab7f0f16dafa2021-12-02T07:41:32ZDissolved oxygen modelling of the Yamuna River using different ANFIS models0273-12231996-973210.2166/wst.2021.466https://doaj.org/article/21b1a106fe9f4b1e82dcab7f0f16dafa2021-11-01T00:00:00Zhttp://wst.iwaponline.com/content/84/10-11/3359https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of the Yamuna River, India, and physiochemical parameters were examined for 5 years to simulate the DO using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system–grid partitioning (ANFIS–GP) and subtractive clustering (ANFIS–SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict DO. Results obtained from the models were evaluated using root mean square error and coefficient of determination (R2) to identify the appropriate combination of parameters to simulate the DO. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS–GP outperforms the ANFIS–SC. M4 generated R2 of 0.953 from ANFIS–GP compared to 0.911 from ANFIS–SC. HIGHLIGHTS ANFIS models were designed to predict the DO of urban steam.; Fuzzy logic allows the classification, data mining, interpretation and optimization of time series data.; Simulation was performed using ANFIS–grid partitioning (ANFIS–GP) and ANFIS–subtractive clustering (ANFIS–SC).; The extensive formulation of the rule base helps identify vital parameters and improves the accuracy of the model.;Sameer AroraAshok K. KeshariIWA Publishingarticledissolved oxygengrid partitioningsubtractive clusteringwater qualityyamuna riverEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 10-11, Pp 3359-3371 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dissolved oxygen grid partitioning subtractive clustering water quality yamuna river Environmental technology. Sanitary engineering TD1-1066
spellingShingle	dissolved oxygen grid partitioning subtractive clustering water quality yamuna river Environmental technology. Sanitary engineering TD1-1066 Sameer Arora Ashok K. Keshari Dissolved oxygen modelling of the Yamuna River using different ANFIS models
description	Dissolved oxygen (DO) is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of DO is necessary to evolve measures for maintaining the riverine ecosystem and designing appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of the Yamuna River, India, and physiochemical parameters were examined for 5 years to simulate the DO using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system–grid partitioning (ANFIS–GP) and subtractive clustering (ANFIS–SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict DO. Results obtained from the models were evaluated using root mean square error and coefficient of determination (R2) to identify the appropriate combination of parameters to simulate the DO. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS–GP outperforms the ANFIS–SC. M4 generated R2 of 0.953 from ANFIS–GP compared to 0.911 from ANFIS–SC. HIGHLIGHTS ANFIS models were designed to predict the DO of urban steam.; Fuzzy logic allows the classification, data mining, interpretation and optimization of time series data.; Simulation was performed using ANFIS–grid partitioning (ANFIS–GP) and ANFIS–subtractive clustering (ANFIS–SC).; The extensive formulation of the rule base helps identify vital parameters and improves the accuracy of the model.;
format	article
author	Sameer Arora Ashok K. Keshari
author_facet	Sameer Arora Ashok K. Keshari
author_sort	Sameer Arora
title	Dissolved oxygen modelling of the Yamuna River using different ANFIS models
title_short	Dissolved oxygen modelling of the Yamuna River using different ANFIS models
title_full	Dissolved oxygen modelling of the Yamuna River using different ANFIS models
title_fullStr	Dissolved oxygen modelling of the Yamuna River using different ANFIS models
title_full_unstemmed	Dissolved oxygen modelling of the Yamuna River using different ANFIS models
title_sort	dissolved oxygen modelling of the yamuna river using different anfis models
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/21b1a106fe9f4b1e82dcab7f0f16dafa
work_keys_str_mv	AT sameerarora dissolvedoxygenmodellingoftheyamunariverusingdifferentanfismodels AT ashokkkeshari dissolvedoxygenmodellingoftheyamunariverusingdifferentanfismodels
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Dissolved oxygen modelling of the Yamuna River using different ANFIS models

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