Multi-objective optimization of aquifer storage and recovery operations under uncertainty via machine learning surrogates

Author:

Vahdat-Aboueshagh Hamid,Tsai Frank T.-C.,Habib Emad,Clement T. Prabhakar

Funder

National Science Foundation Experimental Program to Stimulate Competitive Research

National Science Foundation

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference86 articles.

1. Akhtar, T., Shoemaker, C.A., 2019. Efficient multi-objective optimization through population-based parallel surrogate search. arXiv preprint arXiv:1903.02167. https://doi.org/10.48550/arXiv.1903.02167.

2. Aquifer storage and recovery (ASR): a strategic cost-effective facility to balance water production and demand for Sharjah;Almulla;Desalination,2005

3. Ampazis, N., Perantonis, S. J., 2000. Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 1, pp. 126-131. DOI: 10.1109/IJCNN.2000.857825.

4. Comparison of machine learning models for predicting fluoride contamination in groundwater;Barzegar;Stoch. Env. Res. Risk Assess.,2017

5. Multi-objective optimization for the design of groundwater supply systems under uncertain parameter distribution;Bau;Pacific J. Optim.,2011

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