Investigating the Local-scale Fluctuations of Groundwater Storage by Using Downscaled GRACE/GRACE-FO JPL Mascon Product Based on Machine Learning (ML) Algorithm-Reference-Cited by-同舟云学术

Investigating the Local-scale Fluctuations of Groundwater Storage by Using Downscaled GRACE/GRACE-FO JPL Mascon Product Based on Machine Learning (ML) Algorithm

Published:2023-04-14 Issue:9 Volume:37 Page:3439-3456
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Khorrami Behnam,Ali Shoaib,Gündüz Orhan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-023-03509-w.pdf

Reference34 articles.

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2. Ay M (2021) Trend tests on maximum rainfall series by a novel approach in the Aegean region, Türkiye. Meteorol Atmos Phys 1–15. https://doi.org/10.1007/s00703-021-00795-0

3. Banerjee C, Kumar DN (2018) Analyzing large-scale hydrologic processes using GRACE and hydrometeorological datasets. Water Resour Manag 32:4409–4423. https://doi.org/10.1007/s11269-018-2070-x

4. Beguería S, Vicente-Serrano SM, Angulo-Martínez M (2010) A multiscalar global drought dataset: the SPEIbase: a new gridded product for the analysis of drought variability and impacts. Bull Am Meteor Soc 91(10):1351–1354. https://doi.org/10.1175/2010BAMS2988.1

5. Breiman L (2001) Random Forests Machine Learning 45(1):5–32. https://doi.org/10.1023/A:1010933404324

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