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Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre./
Author:	Davila Hernandez, Cesar E.
Description:	1 online resource (130 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
Subject:	Civil engineering. -
Online resource:	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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245 1 0 $a Machine Learning Based Surrogate Model for Hurricane Storm Surge Forecasting in the Laguna Madre.
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300 $a 1 online resource (130 pages)
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500 $a Source: Masters Abstracts International, Volume: 84-02.
500 $a Advisor: Ho, Jungseok; Kim, Dongchul.
502 $a Thesis (M.S.)--The University of Texas Rio Grande Valley, 2022.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Civil engineering. $3 860360
650 4 $a Water resources management. $3 794747
650 4 $a Ocean engineering. $3 660731
653 $a ADCIRC simulations
653 $a Convolutional neural networks
653 $a Deep learning
653 $a Generative adversarial networks
653 $a Long-short term memory
653 $a Hurricane storm surge
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0595
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Texas Rio Grande Valley. $b Department of Civil Engineering. $3 3683839
773 0 $t Masters Abstracts International $g 84-02.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28773132 $z click for full text (PQDT)