Parameter estimations of a storm surge model using a genetic algorithm
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 param...
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| Published in | Natural hazards (Dordrecht) Vol. 60; no. 3; pp. 1157 - 1165 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.02.2012
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0921-030X 1573-0840 |
| DOI | 10.1007/s11069-011-9900-y |
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| Abstract | 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. |
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| AbstractList | 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. 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. Adapted from the source document. 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.[PUBLICATION ABSTRACT] |
| Author | Lee, Woo Jeong You, Sung Hyup Lee, Yong Hee |
| Author_xml | – sequence: 1 givenname: Sung Hyup surname: You fullname: You, Sung Hyup email: shyou@kma.go.kr organization: Marine Meteorology Division, Observation Infrastructure Bureau/KMA – sequence: 2 givenname: Yong Hee surname: Lee fullname: Lee, Yong Hee organization: Forecast Research Laboratory, National Institute of Meteorological Research/KMA – sequence: 3 givenname: Woo Jeong surname: Lee fullname: Lee, Woo Jeong organization: Global Environment System Research Laboratory, National Institute of Meteorological Research/KMA |
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| CitedBy_id | crossref_primary_10_1088_1742_6596_2718_1_012027 crossref_primary_10_1002_2016JD025151 crossref_primary_10_1016_j_oceaneng_2022_110723 crossref_primary_10_1007_s00500_019_03775_0 crossref_primary_10_1016_j_wace_2019_100196 crossref_primary_10_1002_joc_6858 |
| Cites_doi | 10.1016/S0045-7825(99)00389-8 10.1016/j.ocemod.2006.03.005 10.1016/0079-6611(86)90043-1 10.1175/1520-0442(2004)017<2828:AESBAT>2.0.CO;2 10.1016/S0278-4343(02)00067-5 10.1023/A:1011157212596 10.1175/1520-0493(1999)127<1497:IOPEOT>2.0.CO;2 10.1029/2006GL026186 10.1016/S0278-4343(01)00071-1 10.1080/07055900.1995.9649546 10.1016/S0278-4343(03)00105-5 10.1175/1520-0426(1992)009<0434:OEDIGA>2.0.CO;2 10.5194/angeo-24-3185-2006 10.1175/JCLI3430.1 10.1029/2002JC001563 10.1029/2005GL023697 10.1080/01490410902869656 |
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| Keywords | STORM Typhoon Sea level Genetic algorithm algorithms models simulation pressure tides viscosity optimization diffusivity prediction performances sea level storms |
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| SubjectTerms | 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 |
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