Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management-Reference-Cited by-同舟云学术

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Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management

Published:2016-08-06 Issue:13 Volume:30 Page:4789-4806
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Tsai Wen-Ping,Chiang Yen-Ming,Huang Jun-Lin,Chang Fi-John

Funder

Water Resources Agency, Taiwan, ROC

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11269-016-1453-0.pdf

Reference31 articles.

1. Antonopoulos VZ, Georgiou PE, Antonopoulos ZV (2015) Dispersion coefficient prediction using empirical models and ANNs. Environ Prog 2(2):379–394

2. Bekesi G, McGuire M, Moiler D (2009) Groundwater allocation using a groundwater level response management method—Gnangara groundwater system, Western Australia. Water Resour Manag 23(9):1665–1683

3. Bozorg-Haddad O, Zarezadeh-Mehrizi M, Abdi-Dehkordi M, Loáiciga HA, Mariño MA (2016) A self-tuning ANN model for simulation and forecasting of surface flows. Water Resour Manag:1–23

4. Castaño S, Sanz D, Gómez-Alday JJ (2013) Sensitivity of a groundwater flow model to both climatic variations and management scenarios in a semi-arid region of SE Spain. Water Resour Manag 27(7):2089–2101

5. Chang FJ, Kao LS, Kuo YM, Liu CW (2010) Artificial neural networks for estimating regional arsenic concentrations in a Blackfoot disease area in Taiwan. J Hydrol 388:65–76

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