Logical Genetic Programming (LGP) Development for Irrigation Water Supply Hedging Under Climate Change Conditions

Abstract

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.

A Genetic Programming–Based Model for Colloid Retention in Fractures

“…Genetic programming (GP) is an evolutionary algorithm that provides the ability to uncover complex mathematical relationships between a set of inputs and an output that is difficult to identify via classic mathematical or statistical techniques (Koza 1994). It has been widely applied in the field of water resources engineering to identify rainfall-runoff relationships (Savic et al 1999;Chadalawada et al 2017), predict stream flow (Ravansalar et al 2017), obtain suitable reservoir operation rules (Ashofteh et al 2017), model sea-level rise and the propagation of algae blooms (Muttil and Chau 2006;Ali Ghorbani et al 2010), and estimate saturated hydraulic conductivity (Parasuraman et al 2007).…”