Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective
The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and...
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| Published in: | Cogent engineering Vol. 9; no. 1 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Abingdon
Cogent
31.12.2022
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects: | |
| ISSN: | 2331-1916, 2331-1916 |
| Online Access: | Get full text |
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| Abstract | The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. |
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| AbstractList | The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. |
| Author | Ortega-Martorell, Sandra Zubaidi, Salah L. Mohammed, Sarah J. Ethaib, Saleem Hashim, Khalid Al-Ansari, Nadhir |
| Author_xml | – sequence: 1 givenname: Sarah J. surname: Mohammed fullname: Mohammed, Sarah J. organization: Wasit University – sequence: 2 givenname: Salah L. surname: Zubaidi fullname: Zubaidi, Salah L. organization: University of Warith Al-Anbiyaa – sequence: 3 givenname: Sandra surname: Ortega-Martorell fullname: Ortega-Martorell, Sandra organization: Liverpool John Moores University – sequence: 4 givenname: Nadhir orcidid: 0000-0002-6790-2653 surname: Al-Ansari fullname: Al-Ansari, Nadhir email: nadhir.alansari@ltu.se organization: Lulea University of Technology – sequence: 5 givenname: Saleem surname: Ethaib fullname: Ethaib, Saleem organization: University of Thi-Qar – sequence: 6 givenname: Khalid surname: Hashim fullname: Hashim, Khalid organization: Babylon University |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-94102$$DView record from Swedish Publication Index |
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| SubjectTerms | Algorithms data pre-processing Environment models Geoteknik hybrid model Lakes Machine learning meta-heuristic algorithms Monitoring Optimization Parameters Research methodology Soil Mechanics Water circulation Water level fluctuations Water level forecasting Water levels Water quality Watersheds |
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| Title | Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective |
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