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Groundwater level p...
Groundwater level prediction using machine learning models: A comprehensive review
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- Tao, Hai (författare)
- School of Electronics and Information Engineering, Ankang University, China; School of Computer Sciences, Baoji University of Arts and Sciences, Shaanxi, China; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
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- Hameed, Mohammed Majeed (författare)
- Department of Civil Engineering, Al-Maaref University College, Ramadi, Iraq
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- Marhoon, Haydar Abdulameer (författare)
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; College of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq
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- Zounemat-Kermani, Mohammad (författare)
- Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
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- Heddam, Salim (författare)
- Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route EL HADAIK, 26 Skikda, BP, Algeria
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- Kim, Sungwon (författare)
- Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, South Korea
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- Sulaiman, Sadeq Oleiwi (författare)
- Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, Iraq
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- Tan, Mou Leong (författare)
- GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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- Sa’adi, Zulfaqar (författare)
- Centre for Environmental Sustainability and Water Security (IPASA), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Sekudai, Johor, Malaysia
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- Mehr, Ali Danandeh (författare)
- Civil Engineering Department, Antalya Bilim University, Antalya, Turkey
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- Allawi, Mohammed Falah (författare)
- Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, Iraq
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- Abba, S.I. (författare)
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria
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- Zain, Jasni Mohamad (författare)
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia; Faculty of Computer and Mathematical Sciences, University Technology MARA, Malaysia
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- Falah, Mayadah W. (författare)
- Building and Construction Engineering Technology Department, AL-Mustaqbal University College, Hillah 51001, Iraq
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- Jamei, Mehdi (författare)
- Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran
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- Bokde, Neeraj Dhanraj (författare)
- Department of Civil and Architectural Engineering, Aarhus University, Denmark
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- Bayatvarkeshi, Maryam (författare)
- Department of Geography and Environmental Management, the faculty of Environment, the university of Waterloo, Canada
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- Al-Mukhtar, Mustafa (författare)
- Civil Engineering Department. University of Technology, Baghdad, Iraq
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- Bhagat, Suraj Kumar (författare)
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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- Tiyasha, Tiyasha (författare)
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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- Khedher, Khaled Mohamed (författare)
- Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia; Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul 8000, Tunisia
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- Shahid, Shamsuddin (författare)
- School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
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- Yaseen, Zaher Mundher (författare)
- Adjunct Research Fellow, USQ’s Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, QLD 4350, Australia; New era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
Nyckelord
- State-of-the-art
- Machine learning
- Groundwater level
- Input parameters
- Prediction performance
- Catchment sustainability
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Tao, Hai
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Hameed, Mohammed ...
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Marhoon, Haydar ...
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Zounemat-Kermani ...
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Heddam, Salim
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Kim, Sungwon
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Sulaiman, Sadeq ...
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Tan, Mou Leong
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Sa’adi, Zulfaqar
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Mehr, Ali Danand ...
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Allawi, Mohammed ...
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Abba, S.I.
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Zain, Jasni Moha ...
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Falah, Mayadah W ...
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Jamei, Mehdi
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Bokde, Neeraj Dh ...
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Bayatvarkeshi, M ...
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Al-Mukhtar, Must ...
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Bhagat, Suraj Ku ...
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Tiyasha, Tiyasha
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Khedher, Khaled ...
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Al-Ansari, Nadhi ...
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Shahid, Shamsudd ...
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Yaseen, Zaher Mu ...
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- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Datavetenskap
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Samhällsbyggnads ...
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och Geoteknik
- Artiklar i publikationen
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Neurocomputing
- Av lärosätet
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Luleå tekniska universitet