Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

• Published in: Cogent engineering Vol. 9; no. 1
• Main Authors: Mohammed, Sarah J., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, Hashim, Khalid
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: 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
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2022.2143051

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.