Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control. (November 2021) - Leon, Arturo S.; Bian, Linlong; Tang, Yun
Abstract: This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management. Highlights: We compare the performance of the GA and PS methods for forecasting optimal flow releases in a multi-storage system for flood control. The results of the GA and PS methods are very similar, however the run time required by PS is significantly smaller than that required by GA. Dynamic water management according to the optimization results can significantly mitigate flooding compared to the case without management. A key factor for flood control is to partially empty the storage systems before the rainfall event and during the initial period of rainfall.