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Streamflow classification by employing various machine learning models for peninsular Malaysia

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous val...

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Main Authors:	AlDahoul N., Momo M.A., Chong K.L., Ahmed A.N., Huang Y.F., Sherif M., El-Shafie A.
Other Authors:	56656478800
Format:	Article
Published:	Nature Research 2024
Subjects:	article forecasting Johor Kelantan machine learning Malaysia Melaka Perlis prediction river Selangor support vector machine time series analysis uncertainty
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id	my.uniten.dspace-33907
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spelling	my.uniten.dspace-339072024-10-14T11:17:25Z Streamflow classification by employing various machine learning models for peninsular Malaysia AlDahoul N. Momo M.A. Chong K.L. Ahmed A.N. Huang Y.F. Sherif M. El-Shafie A. 56656478800 57441948400 57208482172 57214837520 55807263900 7005414714 16068189400 article forecasting Johor Kelantan machine learning Malaysia Melaka Perlis prediction river Selangor support vector machine time series analysis uncertainty Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3�days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2�days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting. � 2023, Springer Nature Limited. Final 2024-10-14T03:17:25Z 2024-10-14T03:17:25Z 2023 Article 10.1038/s41598-023-41735-9 2-s2.0-85169680347 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169680347&doi=10.1038%2fs41598-023-41735-9&partnerID=40&md5=7baecf23595cf2b1edad649479d0d0f4 https://irepository.uniten.edu.my/handle/123456789/33907 13 1 14574 All Open Access Gold Open Access Green Open Access Nature Research 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/
topic	article forecasting Johor Kelantan machine learning Malaysia Melaka Perlis prediction river Selangor support vector machine time series analysis uncertainty
spellingShingle	article forecasting Johor Kelantan machine learning Malaysia Melaka Perlis prediction river Selangor support vector machine time series analysis uncertainty AlDahoul N. Momo M.A. Chong K.L. Ahmed A.N. Huang Y.F. Sherif M. El-Shafie A. Streamflow classification by employing various machine learning models for peninsular Malaysia
description	Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3�days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2�days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting. � 2023, Springer Nature Limited.
author2	56656478800
author_facet	56656478800 AlDahoul N. Momo M.A. Chong K.L. Ahmed A.N. Huang Y.F. Sherif M. El-Shafie A.
format	Article
author	AlDahoul N. Momo M.A. Chong K.L. Ahmed A.N. Huang Y.F. Sherif M. El-Shafie A.
author_sort	AlDahoul N.
title	Streamflow classification by employing various machine learning models for peninsular Malaysia
title_short	Streamflow classification by employing various machine learning models for peninsular Malaysia
title_full	Streamflow classification by employing various machine learning models for peninsular Malaysia
title_fullStr	Streamflow classification by employing various machine learning models for peninsular Malaysia
title_full_unstemmed	Streamflow classification by employing various machine learning models for peninsular Malaysia
title_sort	streamflow classification by employing various machine learning models for peninsular malaysia
publisher	Nature Research
publishDate	2024
_version_	1814061030565740544
score	13.222552

Streamflow classification by employing various machine learning models for peninsular Malaysia

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