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Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples

Global warming is predicted to lead to a new geographic and spatial distribution of storm-surge events and an increase in their activity intensity. Therefore, it is necessary to detect storm-surge events in order to reveal temporal and spatial variations in their activity intensity. This study attem...

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Autores principales: Ma, Peipei, Li, Guosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964892/
https://www.ncbi.nlm.nih.gov/pubmed/36833945
http://dx.doi.org/10.3390/ijerph20043253
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author Ma, Peipei
Li, Guosheng
author_facet Ma, Peipei
Li, Guosheng
author_sort Ma, Peipei
collection PubMed
description Global warming is predicted to lead to a new geographic and spatial distribution of storm-surge events and an increase in their activity intensity. Therefore, it is necessary to detect storm-surge events in order to reveal temporal and spatial variations in their activity intensity. This study attempted to detect storm-surge events from the perspective of detecting outliers. Four common outlier-detection methods, the Pauta criterion (PC), Chauvenet criterion (CC), Pareto distribution (PD) and kurtosis coefficient (KC), were used to detect the storm-surge events from the hourly residual water level data of 14 tide gauges along the coasts of China. This paper evaluates the comprehensive ability of the four methods to detect storm-surge events by combining historical typhoon-storm-surge events and deep-learning target-detection-evaluation indicators. The results indicate that (1) all of the four methods are feasible for detecting storm surge events; (2) the PC has the highest comprehensive detection ability for storm-surge events (F1 = 0.66), making it the most suitable for typhoon-storm-surge detection in coastal areas of China; the CC has the highest detection accuracy for typhoon-storm-surge events (precision = 0.89), although the recall of the CC is the lowest (recall = 0.42), as only severe storm surges were detected. This paper therefore evaluates four storm-surge-detection methods in coastal areas of China and provides a basis for the evaluation of storm-surge-detection methods and detection algorithms.
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spelling pubmed-99648922023-02-26 Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples Ma, Peipei Li, Guosheng Int J Environ Res Public Health Article Global warming is predicted to lead to a new geographic and spatial distribution of storm-surge events and an increase in their activity intensity. Therefore, it is necessary to detect storm-surge events in order to reveal temporal and spatial variations in their activity intensity. This study attempted to detect storm-surge events from the perspective of detecting outliers. Four common outlier-detection methods, the Pauta criterion (PC), Chauvenet criterion (CC), Pareto distribution (PD) and kurtosis coefficient (KC), were used to detect the storm-surge events from the hourly residual water level data of 14 tide gauges along the coasts of China. This paper evaluates the comprehensive ability of the four methods to detect storm-surge events by combining historical typhoon-storm-surge events and deep-learning target-detection-evaluation indicators. The results indicate that (1) all of the four methods are feasible for detecting storm surge events; (2) the PC has the highest comprehensive detection ability for storm-surge events (F1 = 0.66), making it the most suitable for typhoon-storm-surge detection in coastal areas of China; the CC has the highest detection accuracy for typhoon-storm-surge events (precision = 0.89), although the recall of the CC is the lowest (recall = 0.42), as only severe storm surges were detected. This paper therefore evaluates four storm-surge-detection methods in coastal areas of China and provides a basis for the evaluation of storm-surge-detection methods and detection algorithms. MDPI 2023-02-13 /pmc/articles/PMC9964892/ /pubmed/36833945 http://dx.doi.org/10.3390/ijerph20043253 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ma, Peipei
Li, Guosheng
Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title_full Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title_fullStr Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title_full_unstemmed Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title_short Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples
title_sort comparison and analysis of detection methods for typhoon-storm surges based on tide-gauge data—taking coasts of china as examples
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964892/
https://www.ncbi.nlm.nih.gov/pubmed/36833945
http://dx.doi.org/10.3390/ijerph20043253
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