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Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China

Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging...

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Main Authors: Deng, Bin, Lai, Sai Hin, Jiang, Changbo, Kumar, Pavitra, El-Shafie, Ahmed, Chin, Ren Jie
Format: Article
Published: Springer Heidelberg 2021
Subjects:
QA75 Electronic computers. Computer science
QE Geology
Online Access:http://eprints.um.edu.my/28209/
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spelling my.um.eprints.282092022-03-05T05:26:56Z http://eprints.um.edu.my/28209/ Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China Deng, Bin Lai, Sai Hin Jiang, Changbo Kumar, Pavitra El-Shafie, Ahmed Chin, Ren Jie QA75 Electronic computers. Computer science QE Geology Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging task. A reliable method that is able to extract the non-linear behaviors of various parameters effectively, and thus enhances the modelling capability in terms of computation time and accuracy is required. Therefore, the Dongting Lake of China, a large-scale river-lake system has been selected for this study. The main aim is to provide a practical method for advanced water level prediction at the downstream outlet of Dongting Lake for flood warning purposes. The novelty of this study is the adoption of a soft computing modelling approach, based on minimum input requirements to reduce its dependency on too many inputs which might limit its functionality in the future. The results obtained show that the model developed can predict the hourly water level in Dongting Lake accurately with an error of 1.2%. It is able to provide an advanced water level prediction of 21 h ahead of the time step, and thus applicable for early flood warning to the surrounding area with densely populated townships. Springer Heidelberg 2021-12 Article PeerReviewed Deng, Bin and Lai, Sai Hin and Jiang, Changbo and Kumar, Pavitra and El-Shafie, Ahmed and Chin, Ren Jie (2021) Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China. Earth Science Informatics, 14 (4). pp. 1987-2001. ISSN 1865-0473, DOI https://doi.org/10.1007/s12145-021-00665-8 <https://doi.org/10.1007/s12145-021-00665-8>. 10.1007/s12145-021-00665-8
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
QE Geology
spellingShingle QA75 Electronic computers. Computer science
QE Geology
Deng, Bin
Lai, Sai Hin
Jiang, Changbo
Kumar, Pavitra
El-Shafie, Ahmed
Chin, Ren Jie
Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
description Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging task. A reliable method that is able to extract the non-linear behaviors of various parameters effectively, and thus enhances the modelling capability in terms of computation time and accuracy is required. Therefore, the Dongting Lake of China, a large-scale river-lake system has been selected for this study. The main aim is to provide a practical method for advanced water level prediction at the downstream outlet of Dongting Lake for flood warning purposes. The novelty of this study is the adoption of a soft computing modelling approach, based on minimum input requirements to reduce its dependency on too many inputs which might limit its functionality in the future. The results obtained show that the model developed can predict the hourly water level in Dongting Lake accurately with an error of 1.2%. It is able to provide an advanced water level prediction of 21 h ahead of the time step, and thus applicable for early flood warning to the surrounding area with densely populated townships.
format Article
author Deng, Bin
Lai, Sai Hin
Jiang, Changbo
Kumar, Pavitra
El-Shafie, Ahmed
Chin, Ren Jie
author_facet Deng, Bin
Lai, Sai Hin
Jiang, Changbo
Kumar, Pavitra
El-Shafie, Ahmed
Chin, Ren Jie
author_sort Deng, Bin
title Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
title_short Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
title_full Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
title_fullStr Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
title_full_unstemmed Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
title_sort advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in dongting lake, china
publisher Springer Heidelberg
publishDate 2021
url http://eprints.um.edu.my/28209/
_version_ 1735409544262057984
score 13.211869

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