Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia
A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river�s water level, on a daily basis based on collected da...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Taylor and Francis Ltd.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-27263 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-272632023-05-29T17:41:47Z Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia Ahmed A.N. Yafouz A. Birima A.H. Kisi O. Huang Y.F. Sherif M. Sefelnasr A. El-Shafie A. 57214837520 57221981418 23466519000 6507051085 55807263900 7005414714 6505592467 16068189400 A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river�s water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Final 2023-05-29T09:41:47Z 2023-05-29T09:41:47Z 2022 Article 10.1080/19942060.2021.2019128 2-s2.0-85124294502 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124294502&doi=10.1080%2f19942060.2021.2019128&partnerID=40&md5=89daaf472096687e48483df97d367b28 https://irepository.uniten.edu.my/handle/123456789/27263 16 1 422 440 All Open Access, Gold Taylor and Francis 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/ |
| description |
A reliable model to predict the changes in the water levels in a river is crucial for better planning to mitigate any risk associated with flooding. In this study, six different Machine Learning (ML) algorithms were developed to predict the river�s water level, on a daily basis based on collected data from 1990 to 2019 which were used to train and test the proposed models. Different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed models with other models. The comparison of several data-driven regression methods indicate that the exponential Gaussian Process Regression (GPR) model offered better accuracy in predicting daily water levels with respect to different assessment criteria. The GPR model was then used to predict the water level after sorting the data based on 10 days maximum and minimum values of the water level, and the results proved the success of this model in catching the extremes of the water levels. In addition to that, based on two uncertainty indicators, it was concluded that the proposed model, the GPR, was capable of predicting the water level of the river with high precision and less uncertainty where the computed using the 95% prediction uncertainty (95PPU) and the d-factor were found to be equal to 98.276 and 0.000525, respectively. The findings of this study show the efficacy of the GPR model in capturing the changes in the water level in a river. Due to the importance of the water level of a river being an parameter for flood monitoring, this technique is likely beneficial to the design of the mitigation strategies for future flooding events. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| author2 |
57214837520 |
| author_facet |
57214837520 Ahmed A.N. Yafouz A. Birima A.H. Kisi O. Huang Y.F. Sherif M. Sefelnasr A. El-Shafie A. |
| format |
Article |
| author |
Ahmed A.N. Yafouz A. Birima A.H. Kisi O. Huang Y.F. Sherif M. Sefelnasr A. El-Shafie A. |
| spellingShingle |
Ahmed A.N. Yafouz A. Birima A.H. Kisi O. Huang Y.F. Sherif M. Sefelnasr A. El-Shafie A. Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| author_sort |
Ahmed A.N. |
| title |
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| title_short |
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| title_full |
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| title_fullStr |
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| title_full_unstemmed |
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia |
| title_sort |
water level prediction using various machine learning algorithms: a case study of durian tunggal river, malaysia |
| publisher |
Taylor and Francis Ltd. |
| publishDate |
2023 |
| _version_ |
1806424150034612224 |
| score |
13.222552 |