Enhanced Artificial Neural Networks Estimating Water Quality Constraints for the Optimal Water Distribution Systems Design-Reference-Cited by-同舟云学术

Enhanced Artificial Neural Networks Estimating Water Quality Constraints for the Optimal Water Distribution Systems Design

Published:2016-09 Issue:9 Volume:142 Page:04016024
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Andrade Manuel A.¹,Choi Christopher Y.²,Lansey Kevin³,Jung Donghwi⁴

Affiliation:

1. Postdoctoral Fellow, Oak Ridge Associated Universities, sponsored by Conservation and Production Research Lab., USDA-ARS, P.O. Drawer 10, Bushland, TX 79102; formerly, Graduate Research Assistant, Dept. of Agricultural and Biosystems Engineering, Univ. of Arizona, Tucson, AZ 85721.

2. Professor, Dept. of Biological Systems Engineering, Univ. of Wisconsin, Madison, WI 53706 (corresponding author).

3. Professor, Dept. of Civil Engineering and Engineering Mechanics, Univ. of Arizona, Tucson, AZ 85721.

4. Postdoctoral Research Associate, Dept. of Civil Engineering and Engineering Mechanics, Univ. of Arizona, Tucson, AZ 85721.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0000663

Reference41 articles.

1. Heuristic Postoptimization Approaches for Design of Water Distribution Systems

2. Least-Cost Design of Water Distribution Networks under Demand Uncertainty

3. Behandish M. and Wu Z. (2012). “GPU-based artificial neural network configuration and training for water distribution system analysis.” Proc. World Environmental and Water Resources Congress ASCE VA.

4. Bi W. and Dandy G. (2012). “Retraining of metamodels for the optimisation of water distribution systems.” Proc. Water Distribution Systems Analysis ASCE VA.

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