Parameter estimations of a storm surge model using a genetic algorithm

• Published in: Natural hazards (Dordrecht) Vol. 60; no. 3; pp. 1157 - 1165
• Main Authors: You, Sung Hyup, Lee, Yong Hee, Lee, Woo Jeong
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2012; Springer; Springer Nature B.V
• Subjects: Algorithms; Civil Engineering; Computer simulation; Disasters; Drag; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental Management; Exact sciences and technology; Genetic algorithms; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Horizontal; Hydrogeology; Marine; Mathematical models; Meteorology; Natural Hazards; Natural hazards: prediction, damages, etc; Ocean; Original Paper; Parameter estimation; Peninsulas; Science; Sea level; Storm surges; Storms; Typhoons; STORM; Typhoon; Sea level; Genetic algorithm; algorithms; models; simulation; pressure; tides; viscosity; optimization; diffusivity; prediction; performances; sea level; storms
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-011-9900-y

A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model (STORM) to improve sea level predictions of storm surges. The model was then tested using data from Typhoon Maemi, which landed on the Korean Peninsula in 2003. The following model parameters were used: the coefficients for bottom drag, background horizontal diffusivity, Smagorinsky’s horizontal viscosity, and sea level pressure scaling. The simulation results using the optimized parameters improved sea level predictions. This study demonstrates that parameter optimizations and their adequate applications are essential for improving model performance.