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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Mohammed, Sarah J.Department of Civil Engineering, Wasit University, Wasit, Iraq (författare)

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

Artikel/kapitelEngelska2022

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2022-11-11
Taylor & Francis Group,2022
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LIBRIS-ID:oai:DiVA.org:ltu-94102
https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-94102URI
https://doi.org/10.1080/23311916.2022.2143051DOI

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Språk:engelska
Sammanfattning på:engelska

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Validerad;2022;Nivå 2;2022-11-14 (hanlid)
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.

Ämnesord och genrebeteckningar

NATURVETENSKAP Geovetenskap och miljövetenskap Oceanografi, hydrologi och vattenresurser hsv//swe
NATURAL SCIENCES Earth and Related Environmental Sciences Oceanography, Hydrology and Water Resources hsv//eng
Water level forecasting
data pre-processing
meta-heuristic algorithms
hybrid model
Soil Mechanics
Geoteknik

Biuppslag (personer, institutioner, konferenser, titlar ...)

Zubaidi, Salah L.Department of Civil Engineering, Wasit University, Wasit, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq (författare)
Ortega-Martorell, SandraDepartment of Applied Mathematics, Liverpool John Moores University, Liverpool, UK (författare)
Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
Ethaib, SaleemDepartment of Civil Engineering, University of Thi-Qar, Al-Nassiriya, Iraq (författare)
Hashim, KhalidSchool of Civil Engineering and Built Environment, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK; Department of Environment Engineering, Babylon University, Babylon 51001, Iraq (författare)
Department of Civil Engineering, Wasit University, Wasit, IraqDepartment of Civil Engineering, Wasit University, Wasit, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq (creator_code:org_t)

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Ingår i:Cogent Engineering: Taylor & Francis Group9:12331-1916

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Av författaren/redakt...: Mohammed, Sarah ...; Zubaidi, Salah L ...; Ortega-Martorell ...; Al-Ansari, Nadhi ...; Ethaib, Saleem; Hashim, Khalid

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