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

Mohammed, Sarah J, Zubaidi, Salah L, Ortega-Martorell, Sandra ORCID: 0000-0001-9927-3209, Al-Ansari, Nadhir, Ethaib, Saleem and Hashim, Khalid (2022) Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective. COGENT ENGINEERING, 9 (1). 2143051-.

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Official URL: http://dx.doi.org/10.1080/23311916.2022.2143051

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.

Item Type:	Article
Uncontrolled Keywords:	Water level forecasting, data pre-processing, meta-heuristic algorithms, hybrid model
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	18 May 2023 09:22
Last Modified:	15 Mar 2024 16:31
DOI:	10.1080/23311916.2022.2143051
Open Access URL:	https://doi.org/10.1080/23311916.2022.2143051
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170471