State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and ManagementSource: Journal of Water Resources Planning and Management:;2010:;Volume ( 136 ):;issue: 004Author:John Nicklow
,
Patrick Reed
,
Dragan Savic
,
Tibebe Dessalegne
,
Laura Harrell
,
Amy Chan-Hilton
,
Mohammad Karamouz
,
Barbara Minsker
,
Avi Ostfeld
,
Abhishek Singh
,
Emily Zechman
DOI: 10.1061/(ASCE)WR.1943-5452.0000053Publisher: American Society of Civil Engineers
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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contributor author | John Nicklow | |
contributor author | Patrick Reed | |
contributor author | Dragan Savic | |
contributor author | Tibebe Dessalegne | |
contributor author | Laura Harrell | |
contributor author | Amy Chan-Hilton | |
contributor author | Mohammad Karamouz | |
contributor author | Barbara Minsker | |
contributor author | Avi Ostfeld | |
contributor author | Abhishek Singh | |
contributor author | Emily Zechman | |
date accessioned | 2017-05-08T22:03:07Z | |
date available | 2017-05-08T22:03:07Z | |
date copyright | July 2010 | |
date issued | 2010 | |
identifier other | %28asce%29wr%2E1943-5452%2E0000101.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/69907 | |
description 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. | |
publisher | American Society of Civil Engineers | |
title | State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management | |
type | Journal Paper | |
journal volume | 136 | |
journal issue | 4 | |
journal title | Journal of Water Resources Planning and Management | |
identifier doi | 10.1061/(ASCE)WR.1943-5452.0000053 | |
tree | Journal of Water Resources Planning and Management:;2010:;Volume ( 136 ):;issue: 004 | |
contenttype | Fulltext |