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Predicting Water Quality Parameters in a Complex River System

This research applied a machine learning technique for predicting the water quality parameters of Kelantan River?using the historical data collected from various stations. Support Vector Machine (SVM) was used to develop the?prediction model. Six water quality parameters (dissolved oxygen (DO), bioc...

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Main Authors:	Kurniawan I., Hayder G., Mustafa H.M.
Other Authors:	56541431000
Format:	Article
Published:	Polish Society of Ecological Engineering (PTIE) 2023
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id	my.uniten.dspace-25635
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spelling	my.uniten.dspace-256352023-05-29T16:12:02Z Predicting Water Quality Parameters in a Complex River System Kurniawan I. Hayder G. Mustafa H.M. 56541431000 56239664100 57217195204 This research applied a machine learning technique for predicting the water quality parameters of Kelantan River?using the historical data collected from various stations. Support Vector Machine (SVM) was used to develop the?prediction model. Six water quality parameters (dissolved oxygen (DO), biochemical oxygen demand (BOD),?chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and suspended solids (SS)) were predicted. The?dataset was obtained from the measurement of 14 stations of Kelantan River from September 2005 to December?2017 with a total sample of 148 monthly data. We defined 3 schemes of prediction to investigate the contribution?of the attribute number and the model performance. The outcome of the study demonstrated that the prediction?of the suspended solid parameter gave the best performance, which was indicated by the highest values of the?R2 score. Meanwhile, the prediction of the COD parameter gave the lowest score of R2 score, indicating the difficulty?of the dataset to be modelled by SVM. The analysis of the contribution of attribute number shows that the?prediction of the four parameters (DO, BOD, NH3-N, and SS) is directly proportional to the performance of the?model. Similarly, the best prediction of the pH parameter is obtained from the utilization of the least number of?attributes found in scheme 1. � 2020. The American Society of Hematology. All Rights Reserved. Final 2023-05-29T08:12:02Z 2023-05-29T08:12:02Z 2020 Article 10.12911/22998993/129579 2-s2.0-85098595909 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098595909&doi=10.12911%2f22998993%2f129579&partnerID=40&md5=e165ee48dc91f9f1eb4bd431d5dd7135 https://irepository.uniten.edu.my/handle/123456789/25635 22 1 250 257 All Open Access, Gold Polish Society of Ecological Engineering (PTIE) Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	This research applied a machine learning technique for predicting the water quality parameters of Kelantan River?using the historical data collected from various stations. Support Vector Machine (SVM) was used to develop the?prediction model. Six water quality parameters (dissolved oxygen (DO), biochemical oxygen demand (BOD),?chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and suspended solids (SS)) were predicted. The?dataset was obtained from the measurement of 14 stations of Kelantan River from September 2005 to December?2017 with a total sample of 148 monthly data. We defined 3 schemes of prediction to investigate the contribution?of the attribute number and the model performance. The outcome of the study demonstrated that the prediction?of the suspended solid parameter gave the best performance, which was indicated by the highest values of the?R2 score. Meanwhile, the prediction of the COD parameter gave the lowest score of R2 score, indicating the difficulty?of the dataset to be modelled by SVM. The analysis of the contribution of attribute number shows that the?prediction of the four parameters (DO, BOD, NH3-N, and SS) is directly proportional to the performance of the?model. Similarly, the best prediction of the pH parameter is obtained from the utilization of the least number of?attributes found in scheme 1. � 2020. The American Society of Hematology. All Rights Reserved.
author2	56541431000
author_facet	56541431000 Kurniawan I. Hayder G. Mustafa H.M.
format	Article
author	Kurniawan I. Hayder G. Mustafa H.M.
spellingShingle	Kurniawan I. Hayder G. Mustafa H.M. Predicting Water Quality Parameters in a Complex River System
author_sort	Kurniawan I.
title	Predicting Water Quality Parameters in a Complex River System
title_short	Predicting Water Quality Parameters in a Complex River System
title_full	Predicting Water Quality Parameters in a Complex River System
title_fullStr	Predicting Water Quality Parameters in a Complex River System
title_full_unstemmed	Predicting Water Quality Parameters in a Complex River System
title_sort	predicting water quality parameters in a complex river system
publisher	Polish Society of Ecological Engineering (PTIE)
publishDate	2023
_version_	1806425940906999808
score	13.211869

Predicting Water Quality Parameters in a Complex River System

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