The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management
Abstract
During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, including a call to address larger-scale problems that are wrought with uncertainty and an expanded need for cross fertilization and collaboration among our field’s subdisciplines. Evolutionary computation will continue to evolve in the future as we encounter increased problem complexities and uncertainty and as the societal pressure for more innovative and efficient solutions rises.
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Cites
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Werner, A. D., Bakker, M., Post, V. E. A., Vandenbohede, A., Lu, C., Ataie-Ashtiani, B., Simmons, C. T., & Barry, D. A. (2013) · Advances in Water Resources · doi:10.1016/j.advwatres.2012.03.004 · Cited by 1,658
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.09.013 · Cited by 657
Self-citationA survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 650
Many objective robust decision making for complex environmental systems undergoing change
Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.12.007 · Cited by 508
Self-citationGlobal change and the groundwater management challenge
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Metadata
- Authors
- Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., Chan-Hilton, A., Karamouz, M., Minsker, B., Ostfeld, A., Singh, A., Zechman, E., & ASCE Task Committee on Evolutionary Computation in Environmental and Water Resources Engineering
- Venue
- Journal of Water Resources Planning and Management
- Published
- 2010-07
- Volume
- 136
- Issue
- 4
- Pages
- 412-432
- ISSN
- 0733-9496 (ISSN-L)
- 1943-5452
- OpenAlex
- W1997738271
- Cited by
- 624
- References
- 5
- FWCI
- 42.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., Chan-Hilton, A., Karamouz, M., Minsker, B., Ostfeld, A., Singh, A., Zechman, E., & ASCE Task Committee on Evolutionary Computation in Environmental and Water Resources Engineering. (2010). State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management. Journal of Water Resources Planning and Management. https://doi.org/10.1061/(asce)wr.1943-5452.0000053
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.