Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors-Reference-Cited by-同舟云学术

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Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors

Published:2018-09-28 Issue:2 Volume:27 Page:567-579
ISSN:1431-2174
Container-title:Hydrogeology Journal
language:en
Short-container-title:Hydrogeol J

Author:

Lee Sanghoon,Lee Kang-Kun,Yoon Heesung

Funder

National Research Foundation of Korea

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Water Science and Technology

Link

http://link.springer.com/content/pdf/10.1007/s10040-018-1866-3.pdf

Reference41 articles.

1. Alley WM, Reilly TE, Franke OL (1999) Sustainability of ground-water resources. US Geological Survey Circular 1186, pp 79. https://pubs.usgs.gov/circ/circ1186/ . Accessed June 2017

2. Anctil F, Michel C, Perrin C, Andreassian V (2004) A soil moisture index as an auxiliary ANN input for stream flow forecasting. J Hydrol 286(1–4):155–167

3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology I: preliminary concepts. J Hydrol Eng 5:115–123

4. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology II: hydrologic applications. J Hydrol Eng 5:124–137

5. Barzegar R, Fijani E, Moghaddam AA, Tziritis E (2017) Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Sci Total Environ 599:20–31

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