Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia

Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBo...

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Main Authors: Adli Zakaria M.N., Ahmed A.N., Abdul Malek M., Birima A.H., Hayet Khan M.M., Sherif M., Elshafie A.
Other Authors: 58480232100
Format: Article
Published: Elsevier Ltd 2024
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MLP
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spelling my.uniten.dspace-342042024-10-14T11:18:25Z Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia Adli Zakaria M.N. Ahmed A.N. Abdul Malek M. Birima A.H. Hayet Khan M.M. Sherif M. Elshafie A. 58480232100 57214837520 57221404206 23466519000 16304362800 7005414714 16068189400 LSTM Machine learning Malaysia MLP Muda river Water level XGBoost Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training. � 2023 The Authors Final 2024-10-14T03:18:25Z 2024-10-14T03:18:25Z 2023 Article 10.1016/j.heliyon.2023.e17689 2-s2.0-85164310557 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164310557&doi=10.1016%2fj.heliyon.2023.e17689&partnerID=40&md5=d67d1dbe6bda2e19af3b5ac83837c937 https://irepository.uniten.edu.my/handle/123456789/34204 9 7 e17689 All Open Access Gold Open Access Elsevier Ltd 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 LSTM
Machine learning
Malaysia
MLP
Muda river
Water level
XGBoost
spellingShingle LSTM
Machine learning
Malaysia
MLP
Muda river
Water level
XGBoost
Adli Zakaria M.N.
Ahmed A.N.
Abdul Malek M.
Birima A.H.
Hayet Khan M.M.
Sherif M.
Elshafie A.
Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
description Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training. � 2023 The Authors
author2 58480232100
author_facet 58480232100
Adli Zakaria M.N.
Ahmed A.N.
Abdul Malek M.
Birima A.H.
Hayet Khan M.M.
Sherif M.
Elshafie A.
format Article
author Adli Zakaria M.N.
Ahmed A.N.
Abdul Malek M.
Birima A.H.
Hayet Khan M.M.
Sherif M.
Elshafie A.
author_sort Adli Zakaria M.N.
title Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
title_short Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
title_full Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
title_fullStr Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
title_full_unstemmed Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
title_sort exploring machine learning algorithms for accurate water level forecasting in muda river, malaysia
publisher Elsevier Ltd
publishDate 2024
_version_ 1814061045841395712
score 13.209306