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State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management.

Authors :
Nicklow, John
Reed, Patrick
Savic, Dragan
Dessalegne, Tibebe
Harrell, Laura
Chan-Hilton, Amy
Karamouz, Mohammad
Minsker, Barbara
Ostfeld, Avi
Singh, Abhishek
Zechman, Emily
Source :
Journal of Water Resources Planning & Management; Jul2010, Vol. 136 Issue 4, p412-432, 21p, 1 Diagram
Publication Year :
2010

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07339496
Volume :
136
Issue :
4
Database :
Complementary Index
Journal :
Journal of Water Resources Planning & Management
Publication Type :
Academic Journal
Accession number :
51415724
Full Text :
https://doi.org/10.1061/(ASCE)WR.1943-5452.0000053