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Logical Genetic Programming (LGP) Development for Irrigation Water Supply Hedging Under Climate Change Conditions
Author(s) -
Ashofteh ParisaSadat,
BozorgHaddad Omid,
Loáiciga Hugo A.
Publication year - 2017
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.2144
Subject(s) - baseline (sea) , genetic programming , vulnerability (computing) , reliability (semiconductor) , economic shortage , climate change , irrigation , genetic algorithm , water supply , environmental science , computer science , mathematics , mathematical optimization , environmental engineering , ecology , artificial intelligence , biology , fishery , thermodynamics , power (physics) , linguistics , physics , philosophy , computer security , government (linguistics)
Traditional genetic programming (TGP) is herein enhanced by the addition of logical operators to form logical genetic programming (LGP). The LGP approach is applied to calculate hedging reservoir‐operation rules for the Aidoghmoush single‐purpose reservoir (north‐eastern Iran) to supply irrigation water during a 14‐year baseline operation period (1987–2000) and a climatically changed condition (2026–2039). The objective function of the hedging rule is to minimize the long‐term shortage ratio (LSR). Our results show that the LGP‐obtained hedging rule compares favourably with that obtained with the TGP approach, so that the former approach's objective function is 25 and 6% better than the latter's approach with the baseline and climate change conditions, respectively. The results obtained concerning the reliability, vulnerability and resiliency of water supply indicate that the LGP hedging operating rule decreases the water supply reliability by 34%, increases the vulnerability by 58%, and decreases the resiliency by 29% during climate change conditions compared with baseline conditions. Copyright © 2017 John Wiley & Sons, Ltd.