SwePub
Sök i LIBRIS databas

  Utökad sökning

id:"swepub:oai:DiVA.org:ltu-89826"
 

Sökning: id:"swepub:oai:DiVA.org:ltu-89826" > Groundwater level p...

Groundwater level prediction using machine learning models: A comprehensive review

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
Hameed, Mohammed Majeed (författare)
Department of Civil Engineering, Al-Maaref University College, Ramadi, Iraq
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
visa fler...
Zounemat-Kermani, Mohammad (författare)
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Heddam, Salim (författare)
Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route EL HADAIK, 26 Skikda, BP, Algeria
Kim, Sungwon (författare)
Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, South Korea
Sulaiman, Sadeq Oleiwi (författare)
Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, Iraq
Tan, Mou Leong (författare)
GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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
Mehr, Ali Danandeh (författare)
Civil Engineering Department, Antalya Bilim University, Antalya, Turkey
Allawi, Mohammed Falah (författare)
Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, Iraq
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
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
Falah, Mayadah W. (författare)
Building and Construction Engineering Technology Department, AL-Mustaqbal University College, Hillah 51001, Iraq
Jamei, Mehdi (författare)
Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran
Bokde, Neeraj Dhanraj (författare)
Department of Civil and Architectural Engineering, Aarhus University, Denmark
Bayatvarkeshi, Maryam (författare)
Department of Geography and Environmental Management, the faculty of Environment, the university of Waterloo, Canada
Al-Mukhtar, Mustafa (författare)
Civil Engineering Department. University of Technology, Baghdad, Iraq
Bhagat, Suraj Kumar (författare)
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Tiyasha, Tiyasha (författare)
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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
Al-Ansari, Nadhir, 1947- (författare)
Luleå tekniska universitet,Geoteknologi
Shahid, Shamsuddin (författare)
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
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
visa färre...
 (creator_code:org_t)
Elsevier, 2022
2022
Engelska.
Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
Relaterad länk:
https://doi.org/10.1...
visa fler...
https://ltu.diva-por... (primary) (Raw object)
https://urn.kb.se/re...
https://doi.org/10.1...
visa färre...
  • Forskningsöversikt (refereegranskat)
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)

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy