Description of a Python script to implement a probabilistic surrogate model for predicting tropical cyclone driven flood depth and extent

Daneshvar, Fariborz; Mani, Soroosh; Pringle, William J.; Moghimi, Saeed; Myers, Edward

doi:10.25923/wyvn-4405

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Description of a Python script to implement a probabilistic surrogate model for predicting tropical cyclone driven flood depth and extent

2024
By Daneshvar, Fariborz ; Mani, Soroosh ; Pringle, William J. ; ...
Series: NOAA technical memorandum NOS CS ; 59

[PDF-2.37 MB]

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Personal Author:

Daneshvar, Fariborz ; Mani, Soroosh ; Pringle, William J. ; Moghimi, Saeed ; Myers, Edward

Daneshvar, Fariborz ; Mani, Soroosh ; Pringle, William J. ; Moghimi, Saeed ; Myers, Edward Less -
Corporate Authors:

Coast Survey Development Laboratory (U.S.) ; Earth Resources Technology. ; Argonne National Laboratory.

Coast Survey Development Laboratory (U.S.) ; Earth Resources Technology. ; Argonne National Laboratory. Less -
NOAA Program & Office:

NOS (National Ocean Service) ; OCS (Office of Coast Survey)

NOS (National Ocean Service) ; OCS (Office of Coast Survey) Less -
Description:

This document introduces a Python script for generating probabilistic predictions of tropical cyclone (TC) driven coastal flooding.
Keywords:

[+]

Coastal Flooding Hurricane Florence (2018) Machine Learning Modeling Probability SCHISM Storm Surge Surrogate Model Uncertainty Quantification
Series:

NOAA technical memorandum NOS CS ; 59
DOI:

https://doi.org/10.25923/wyvn-4405
Format:

PDF
Publisher:

NOAA National Ocean Service ; NOAA Office of Coast Survey

NOAA National Ocean Service ; NOAA Office of Coast Survey Less -
Document Type:

Technical Memorandum
License:

https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC0 Public Domain
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Submitted
Main Document Checksum:

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urn:sha-512:92736be25b2086e0fb088926084c0d9e3456f22c4e3ef1a1dc6f747f899c6c426fee5bb1863632dc6d8645d077aa3716ea67e9945fc8ac213e70d4456e58da62
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https://repository.library.noaa.gov/view/noaa/66123/noaa_66123_DS1.pdf
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Description of a Python script to implement a probabilistic surrogate model for predicting tropical cyclone driven flood depth and extent

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