Integration of Node Classification in Storm Surge Surrogate Modeling
نویسندگان
چکیده
Surrogate models, also referenced as metamodels, have emerged attractive data-driven, predictive models for storm surge estimation. They are calibrated based on an existing database of synthetic simulations and can provide fast-to-compute approximations the expected surge, replacing numerical model that was used to establish this database. This paper discusses specifically development a kriging metamodel prediction peak surges. For nearshore nodes remained dry in some simulations, necessary first step, before calibration, is imputation address missing data corresponding such instances estimate so-called pseudo-surge. typically performed using geospatial interpolation technique, with k nearest-neighbor (kNN) being one chosen purpose paper. The pseudo-surge estimates obtained from may lead erroneous classification instances, classified inundated (pseudo-surge greater than node elevation), even though they were actually dry. integration secondary surrogate recently proposed challenges associated information. contribution further examines above offers several advances. benefits implementing carefully examined across different characteristics, revealing important trends necessity integrating classifier predictions. Additionally, combination two probabilistic characterization classification, instead deterministic one, considered. illustrate advances corresponds 645 tropical cyclones (TCs) developed flood study Louisiana region. fact various protective measures present region creates interesting scenarios respect groups remain storms behind these protected zones. Advances kNN methodology, imputation, presented unique features, considering connectivity within hydrodynamic simulation model.
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ژورنال
عنوان ژورنال: Journal of Marine Science and Engineering
سال: 2022
ISSN: ['2077-1312']
DOI: https://doi.org/10.3390/jmse10040551