Groundwater levels estimation from GRACE/GRACE-FO and hydro-meteorological data using deep learning in Ganga River basin, India-Reference-Cited by-同舟云学术

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Groundwater levels estimation from GRACE/GRACE-FO and hydro-meteorological data using deep learning in Ganga River basin, India

Published:2023-09-02 Issue:19 Volume:82 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Moudgil Pragay Shourya,Rao G. Srinivasa

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Link

https://link.springer.com/content/pdf/10.1007/s12665-023-11137-1.pdf

Reference68 articles.

1. Agarwal V, Akyilmaz O, Shum CK, Feng W, Haritashya U, Chen W (2022) Machine Learning application for modeling high-resolution groundwater storage variations in North China Plain. Res Square. https://doi.org/10.21203/rs.3.rs-2062965/v1

2. Asoka A, Gleeson T, Wada Y, Mishra V (2017) Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India. Nat Geosci 10(2):109–117

3. Asoka A, Wada Y, Fishman R, Mishra V (2018) Strong linkage between precipitation intensity and monsoon season groundwater recharge in India. Geophys Res Lett 45(11):5536–5544

4. Bhanja SN, Mukherjee A, Saha D, Velicogna I, Famiglietti JS (2016) Validation of GRACE based groundwater storage anomaly using in-situ groundwater level measurements in India. J Hydrol 543:729–738

5. Bhanja SN, Mukherjee A, Rodell M (2020) Groundwater storage change detection from in situ and GRACE-based estimates in major river basins across India. Hydrol Sci J 65(4):650–659

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1. Monitoring Terrestrial Water Storage Using GRACE/GRACE-FO Data over India: A Review;Iranian Journal of Science and Technology, Transactions of Civil Engineering;2024-09-12

2. Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review;Groundwater for Sustainable Development;2024-05

3. Application of the improved dung beetle optimizer, muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area, China;Atmospheric and Oceanic Science Letters;2024-04

4. Bridging the Temporal Gaps in GRACE/GRACE–FO Terrestrial Water Storage Anomalies over the Major Indian River Basins Using Deep Learning;Natural Resources Research;2024-02-22

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