Staff View: Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of sea...

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Main Authors:	Wong, Yong Jie, Shimizu, Yoshihisa, Kamiya, Akinori, Maneechot, Luksanaree, Bharambe, Khagendra Pralhad, Fong, Chng Saun, Nik Sulaiman, Nik Meriam
Format:	Article
Published:	Springer 2021
Subjects:	Q Science (General)
Online Access:	http://eprints.um.edu.my/28593/
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spelling	my.um.eprints.285932022-03-02T07:07:55Z http://eprints.um.edu.my/28593/ Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia Wong, Yong Jie Shimizu, Yoshihisa Kamiya, Akinori Maneechot, Luksanaree Bharambe, Khagendra Pralhad Fong, Chng Saun Nik Sulaiman, Nik Meriam Q Science (General) Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset. Springer 2021-07 Article PeerReviewed Wong, Yong Jie and Shimizu, Yoshihisa and Kamiya, Akinori and Maneechot, Luksanaree and Bharambe, Khagendra Pralhad and Fong, Chng Saun and Nik Sulaiman, Nik Meriam (2021) Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia. Environmental Monitoring and Assessment, 193 (7). ISSN 0167-6369, DOI https://doi.org/10.1007/s10661-021-09202-y <https://doi.org/10.1007/s10661-021-09202-y>. 10.1007/s10661-021-09202-y
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	Q Science (General)
spellingShingle	Q Science (General) Wong, Yong Jie Shimizu, Yoshihisa Kamiya, Akinori Maneechot, Luksanaree Bharambe, Khagendra Pralhad Fong, Chng Saun Nik Sulaiman, Nik Meriam Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
description	Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.
format	Article
author	Wong, Yong Jie Shimizu, Yoshihisa Kamiya, Akinori Maneechot, Luksanaree Bharambe, Khagendra Pralhad Fong, Chng Saun Nik Sulaiman, Nik Meriam
author_facet	Wong, Yong Jie Shimizu, Yoshihisa Kamiya, Akinori Maneechot, Luksanaree Bharambe, Khagendra Pralhad Fong, Chng Saun Nik Sulaiman, Nik Meriam
author_sort	Wong, Yong Jie
title	Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
title_short	Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
title_full	Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
title_fullStr	Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
title_full_unstemmed	Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia
title_sort	application of artificial intelligence methods for monsoonal river classification in selangor river basin, malaysia
publisher	Springer
publishDate	2021
url	http://eprints.um.edu.my/28593/
_version_	1735409561340215296
score	13.211869

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

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