Effectiveness of machine learning ensemble models in assessing groundwater potential in Lidder watershed, India
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01237-8.pdf
Reference46 articles.
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2. Arabameri A, Pal SC, Rezaie F, Nalivan OA, Chowdhuri I, Saha A, Lee S, Moayedi H (2021) Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. J Hydrol Reg Stud 36:100848. https://doi.org/10.1016/j.ejrh.2021.100848
3. Bayat M, Ghorbanpour M, Zare R, Jaafari A, Thai Pham B (2019) Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput Electron Agric 164:104929. https://doi.org/10.1016/j.compag.2019.104929
4. Bloomfield JP, Lewis MA, Newell AJ, Loveless SE, Stuart ME (2020) Characterising variations in the salinity of deep groundwater systems: a case study from Great Britain (GB). J Hydrol Reg Stud 28:100684. https://doi.org/10.1016/j.ejrh.2020.100684
5. Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Bin AB (2020) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602. https://doi.org/10.1016/j.jhydrol.2020.124602
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