Nozari, Soheil, Bailey, Ryan T., Haacker, Erin M.K., Zambreski, Zachary T., Xiang, Zaichen, Lin, Xiaomao (2022) Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions. Journal of Hydrology, 614. 128511 doi:10.1016/j.jhydrol.2022.128511
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions | ||
| Journal | Journal of Hydrology | ||
| Authors | Nozari, Soheil | Author | |
| Bailey, Ryan T. | Author | ||
| Haacker, Erin M.K. | Author | ||
| Zambreski, Zachary T. | Author | ||
| Xiang, Zaichen | Author | ||
| Lin, Xiaomao | Author | ||
| Year | 2022 (November) | Volume | 614 |
| Publisher | Elsevier BV | ||
| DOI | doi:10.1016/j.jhydrol.2022.128511Search in ResearchGate | ||
| Generate Citation Formats | |||
| Mindat Ref. ID | 15464673 | Long-form Identifier | mindat:1:5:15464673:8 |
| GUID | 0 | ||
| Full Reference | Nozari, Soheil, Bailey, Ryan T., Haacker, Erin M.K., Zambreski, Zachary T., Xiang, Zaichen, Lin, Xiaomao (2022) Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions. Journal of Hydrology, 614. 128511 doi:10.1016/j.jhydrol.2022.128511 | ||
| Plain Text | Nozari, Soheil, Bailey, Ryan T., Haacker, Erin M.K., Zambreski, Zachary T., Xiang, Zaichen, Lin, Xiaomao (2022) Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions. Journal of Hydrology, 614. 128511 doi:10.1016/j.jhydrol.2022.128511 | ||
| In | (2022) Journal of Hydrology Vol. 614. Elsevier BV | ||
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