• Sorted by Date • Sorted by Last Name of First Author •
Arshad, Arfan, Mirchi, Ali, Vilcaez, Javier, Umar Akbar, Muhammad, and Madani, Kaveh, 2024. Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin. Journal of Hydrology, 628:130535, doi:10.1016/j.jhydrol.2023.130535.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024JHyd..62830535A, author = {{Arshad}, Arfan and {Mirchi}, Ali and {Vilcaez}, Javier and {Umar Akbar}, Muhammad and {Madani}, Kaveh}, title = "{Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin}", journal = {Journal of Hydrology}, keywords = {Groundwater level, GRACE, machine learning (ML), Geostatistical method, Gap-filled data, Local covariates, Indus Basin}, year = 2024, month = jan, volume = {628}, eid = {130535}, pages = {130535}, doi = {10.1016/j.jhydrol.2023.130535}, adsurl = {https://ui.adsabs.harvard.edu/abs/2024JHyd..62830535A}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }
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