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Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre./
作者:
Davila Hernandez, Cesar E.
面頁冊數:
1 online resource (130 pages)
附註:
Source: Masters Abstracts International, Volume: 84-02.
Contained By:
Masters Abstracts International84-02.
標題:
Civil engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28773132click for full text (PQDT)
ISBN:
9798841722977
Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
Davila Hernandez, Cesar E.
Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
- 1 online resource (130 pages)
Source: Masters Abstracts International, Volume: 84-02.
Thesis (M.S.)--The University of Texas Rio Grande Valley, 2022.
Includes bibliographical references
Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand the application of these methods for the modelling of complex problems such as storm surge. This study gathers historical water level data from coastal buoy stations, uses gridded forecasted weather datasets, and builds a database of ADCIRC hydrodynamic simulations to create a machine learning based surrogate model to provide timely, non-computationally intensive and accurate storm surge predictions for the Lower Laguna Madre in Texas.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798841722977Subjects--Topical Terms:
860360
Civil engineering.
Subjects--Index Terms:
ADCIRC simulationsIndex Terms--Genre/Form:
542853
Electronic books.
Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
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Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
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Source: Masters Abstracts International, Volume: 84-02.
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Advisor: Ho, Jungseok; Kim, Dongchul.
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Thesis (M.S.)--The University of Texas Rio Grande Valley, 2022.
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Includes bibliographical references
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Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand the application of these methods for the modelling of complex problems such as storm surge. This study gathers historical water level data from coastal buoy stations, uses gridded forecasted weather datasets, and builds a database of ADCIRC hydrodynamic simulations to create a machine learning based surrogate model to provide timely, non-computationally intensive and accurate storm surge predictions for the Lower Laguna Madre in Texas.
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Electronic reproduction.
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Ann Arbor, Mich. :
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ProQuest,
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Deep learning
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Long-short term memory
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