Groundwater level prediction using machine learning models:A comprehensive review

Autor: Tao, Hai, Hameed, Mohammed Majeed, Marhoon, Haydar Abdulameer, Zounemat-Kermani, Mohammad, Heddam, Salim, Kim, Sungwon, Sulaiman, Sadeq Oleiwi, Tan, Mou Leong, Sa’adi, Zulfaqar, Mehr, Ali Danandeh, Allawi, Mohammed Falah, Abba, S.I., Zain, Jasni Mohamad, Falah, Mayadah W., Jamei, Mehdi, Bokde, Neeraj Dhanraj, Bayatvarkeshi, Maryam, Al-Mukhtar, Mustafa, Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Khedher, Khaled Mohamed, Al-Ansari, Nadhir, Shahid, Shamsuddin, Yaseen, Zaher Mundher

Publikováno v: Tao, H, Hameed, M M, Marhoon, H A, Zounemat-Kermani, M, Heddam, S, Sungwon, K, Sulaiman, S O, Tan, M L, Sa'adi, Z, Mehr, A D, Allawi, M F, Abba, S I, Zain, J M, Falah, M W, Jamei, M, Bokde, N D, Bayatvarkeshi, M, Al-Mukhtar, M, Bhagat, S K, Tiyasha, T, Khedher, K M, Al-Ansari, N, Shahid, S & Yaseen, Z M 2022, ' Groundwater level prediction using machine learning models : A comprehensive review ', Neurocomputing, vol. 489, pp. 271-308 . https://doi.org/10.1016/j.neucom.2022.03.014

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 machin

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https://pure.au.dk/portal/da/publications/groundwater-level-prediction-using-machine-learning-models(1f04bc64-1f54-4716-be2d-ee372c6bb500).html

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