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

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Abstract

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.

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Availability of data and material

The raw data that support the findings of this study are obtained from the Department of Environment, Malaysia. Restrictions apply to the availability of these data, which were used under permission for this study. Data are available from the authors upon reasonable request and with the permission of Department of Environment, Malaysia.

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Acknowledgements

The authors would like to express their gratitude to the Malaysia’s DOE for permitting the use of the Selangor river basin water quality data for this research purpose. We thank the two anonymous reviewers and the associate editor Prof. Andrew S. Hursthouse for their constructive comments which helped to improve the paper significantly.

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Authors and Affiliations

  1. Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga, 520-0811, Japan

    Yong Jie Wong, Yoshihisa Shimizu, Akinori Kamiya, Luksanaree Maneechot & Khagendra Pralhad Bharambe

  2. International Environment Department, Nippon Koei Co., Ltd, Tokyo, Japan

    Akinori Kamiya

  3. Institute for Advanced Studies, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chng Saun Fong

  4. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Nik Meriam Nik Sulaiman

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Contributions

Yong Jie Wong: conceptualization, methodology, formal Analysis, data curation, software, writing—original draft preparation; Yoshihisa Shimizu: conceptualization, methodology, supervision, writing—review and editing; Akinori Kamiya: conceptualization, formal analysis, software, writing—original draft preparation; Luksanaree Maneechot: software, formal analysis, writing—review and editing; Khagendra Bharambe: software, validation, writing—review and editing; Chng Saun Fong: conceptualization, methodology, writing—review and editing; Nik Meriam Nik Sulaiman: data curation, resources, supervision, writing—review and editing.

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Wong, Y.J., Shimizu, Y., Kamiya, A. et al. Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia. Environ Monit Assess 193, 438 (2021). https://doi.org/10.1007/s10661-021-09202-y

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