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Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms ? Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector R...

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Main Authors:	Pathan A.I., Sidek L.B.M., Basri H.B., Hassan M.Y., Khebir M.I.A.B., Omar S.M.B.A., Khambali M.H.B.M., Torres A.M., Najah Ahmed A.
Other Authors:	57209510674
Format:	Article
Published:	Ain Shams University 2025
Subjects:	Adaptive boosting Dams Forecasting Forestry Hydrology Information management Mean square error Rain Support vector machines Water management Water supply Comparative assessment Effective tool Hydrological process Machine learning techniques Machine-learning Mean absolute error Multilayers perceptrons Root mean squared errors Support vector regressions Water level prediction Water levels
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spelling	my.uniten.dspace-364892025-03-03T15:42:41Z Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques Pathan A.I. Sidek L.B.M. Basri H.B. Hassan M.Y. Khebir M.I.A.B. Omar S.M.B.A. Khambali M.H.B.M. Torres A.M. Najah Ahmed A. 57209510674 35070506500 57065823300 57932252900 57485745900 57964112400 58934910500 54963844600 57214837520 Adaptive boosting Dams Forecasting Forestry Hydrology Information management Mean square error Rain Support vector machines Water management Water supply Comparative assessment Effective tool Hydrological process Machine learning techniques Machine-learning Mean absolute error Multilayers perceptrons Root mean squared errors Support vector regressions Water level prediction Water levels Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms ? Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF) ? in predicting water levels using rainfall data at the Batu Dam, Malaysia. Situated about 16 km from Kuala Lumpur city center, the Batu Dam plays a crucial role in flood mitigation and water supply. Utilizing a statistical approach, the models were evaluated based on key performance metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). Preliminary results accentuated the superior predictive prowess of the MLP model, especially for challenging forecasting scenarios with longer lag intervals. This investigation not only accentuates the potential of data-driven methodologies in hydrology but also offers valuable insights for water resource management in the region. When all scenarios for the MLP model are considered, it is observed that the 3-day scenario performed the best within MLP, with the lowest RMSE (at 0.0072) and MAE (at 0.005), and the highest R2 score (at 0.9972). Furthermore, within the MLP model. Due to its exceptionally high performance, the MLP-3 model proved to be an excellent choice for our modeling purposes. Furthermore, it was observed that MLP-3 yields a high R2 score of 0.994, and its predictions aligned closely with the actual water level values. This indicates that the model fits very well to the modeling problem. On the other hand, the SVR-30 model had an R2 score of 0.83, and its predictions were quite scattered with respect to the actual water levels. Four different input scenarios were investigated, considering correlation analysis. Generally, the comparison of four ML model indicated that the MLP model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The findings of this study depicted the accomplishment of MLP model in capturing the changes in the water level of a dam thus paving the way for which the model can be used in works to mitigate potential risk that may occur in the future from natural events. ? 2024 THE AUTHORS Final 2025-03-03T07:42:41Z 2025-03-03T07:42:41Z 2024 Article 10.1016/j.asej.2024.102854 2-s2.0-85193432621 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193432621&doi=10.1016%2fj.asej.2024.102854&partnerID=40&md5=05bbafe1df910a950dbcefa25735bf26 https://irepository.uniten.edu.my/handle/123456789/36489 15 7 102854 All Open Access; Gold Open Access Ain Shams University 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	Adaptive boosting Dams Forecasting Forestry Hydrology Information management Mean square error Rain Support vector machines Water management Water supply Comparative assessment Effective tool Hydrological process Machine learning techniques Machine-learning Mean absolute error Multilayers perceptrons Root mean squared errors Support vector regressions Water level prediction Water levels
spellingShingle	Adaptive boosting Dams Forecasting Forestry Hydrology Information management Mean square error Rain Support vector machines Water management Water supply Comparative assessment Effective tool Hydrological process Machine learning techniques Machine-learning Mean absolute error Multilayers perceptrons Root mean squared errors Support vector regressions Water level prediction Water levels Pathan A.I. Sidek L.B.M. Basri H.B. Hassan M.Y. Khebir M.I.A.B. Omar S.M.B.A. Khambali M.H.B.M. Torres A.M. Najah Ahmed A. Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
description	Machine learning (ML) techniques are rapidly emerging as effective tools in predicting complex hydrological processes. The present study aims to comparatively assess the efficacy of four machine learning algorithms ? Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF) ? in predicting water levels using rainfall data at the Batu Dam, Malaysia. Situated about 16 km from Kuala Lumpur city center, the Batu Dam plays a crucial role in flood mitigation and water supply. Utilizing a statistical approach, the models were evaluated based on key performance metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). Preliminary results accentuated the superior predictive prowess of the MLP model, especially for challenging forecasting scenarios with longer lag intervals. This investigation not only accentuates the potential of data-driven methodologies in hydrology but also offers valuable insights for water resource management in the region. When all scenarios for the MLP model are considered, it is observed that the 3-day scenario performed the best within MLP, with the lowest RMSE (at 0.0072) and MAE (at 0.005), and the highest R2 score (at 0.9972). Furthermore, within the MLP model. Due to its exceptionally high performance, the MLP-3 model proved to be an excellent choice for our modeling purposes. Furthermore, it was observed that MLP-3 yields a high R2 score of 0.994, and its predictions aligned closely with the actual water level values. This indicates that the model fits very well to the modeling problem. On the other hand, the SVR-30 model had an R2 score of 0.83, and its predictions were quite scattered with respect to the actual water levels. Four different input scenarios were investigated, considering correlation analysis. Generally, the comparison of four ML model indicated that the MLP model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The findings of this study depicted the accomplishment of MLP model in capturing the changes in the water level of a dam thus paving the way for which the model can be used in works to mitigate potential risk that may occur in the future from natural events. ? 2024 THE AUTHORS
author2	57209510674
author_facet	57209510674 Pathan A.I. Sidek L.B.M. Basri H.B. Hassan M.Y. Khebir M.I.A.B. Omar S.M.B.A. Khambali M.H.B.M. Torres A.M. Najah Ahmed A.
format	Article
author	Pathan A.I. Sidek L.B.M. Basri H.B. Hassan M.Y. Khebir M.I.A.B. Omar S.M.B.A. Khambali M.H.B.M. Torres A.M. Najah Ahmed A.
author_sort	Pathan A.I.
title	Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
title_short	Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
title_full	Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
title_fullStr	Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
title_full_unstemmed	Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques
title_sort	comparative assessment of rainfall-based water level prediction using machine learning (ml) techniques
publisher	Ain Shams University
publishDate	2025
_version_	1825816274218778624
score	13.244413

Comparative assessment of rainfall-based water level prediction using machine learning (ML) techniques

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