A Predictive Model for Estimating Damage from Wind Waves during Coastal Storms

Choo, Yeon Moon; Chun, Kun Hak; Jeon, Hae Seong; Sim, Sang-Bo

doi:10.3390/w13091322

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The low wind speed is related to the occurrence of sea fog in South Korea [52]. In addition, high wind speeds can cause high waves that can cause severe damage to coastal areas [53,54]. Accurate observation can promote prediction of threats by sea fog and high waves.…”

Section: Discussionmentioning

confidence: 99%

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

et al. 2021

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Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied.

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Section: Discussionmentioning

confidence: 99%

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

et al. 2021

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Machine Learning Models for Storm Damage Prediction in Nova Scotia, Canada

Lokhandwala,

Saif,

Ghasemi

2023

2023 International Conference on Decision Aid Sciences and Applications (DASA)

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Game Theory-Based Comparison of Disaster Risk Assessment for Two Landfall Typhoons: A Case Study of Jilin Province’s Impact

Dong,

Zhu,

Zhang

et al. 2024

Atmosphere

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Utilizing the best typhoon track data, district and county scale disaster data in Jilin Province, meteorological data, and geographical data, the combined weighting method of AHP-EWM (Analytic Hierarchy Process–Entropy Weight Method) and game theory is employed to conduct a comprehensive risk analysis and comparison of the disaster risk caused by two typhoons, Maysak and Haishen, in Jilin Province. Game theory enhances precision in evaluation beyond conventional approaches, effectively addressing the shortcomings of both subjective and objective weighting methods. Typhoon Maysak and Typhoon Haishen exhibit analogous tracks. They have successively exerted an impact on Jilin Province, and the phenomenon of overlapping rain areas is a crucial factor in triggering disasters. Typhoon Maysak features stronger wind force and greater hourly rainfall intensity, while Typhoon Haishen has a longer duration of rainfall. Additionally, Typhoon Maysak causes more severe disasters in Jilin Province. With regard to the four dimensions of disaster risk, the analysis of hazards reveals that the areas categorized as high risk and above in relation to the two typhoons are mainly located in the central-southern and eastern regions of Jilin Province. Typhoon Maysak has a slightly higher hazard level. During the exposure assessment, it was determined that the high-risk areas occupied 16% of the gross area of Jilin Province. It is mainly concentrated in three economically developed cities, as well as some large agricultural counties. In the context of vulnerability analysis, regions classified as high risk and above constitute 54% of the overall area. The areas classified as having high vulnerability are predominantly located in Yushu, Nong’an, and Songyuan. From the analysis of emergency response and recovery ability, Changchun has strong typhoon disaster prevention and reduction ability. This is proportional to the local level of economic development. The mountainous areas in the east and the regions to the west are comparatively weak. Finally, the comprehensive typhoon disaster risk zoning indicates that the zoning of the two typhoons is relatively comparable. When it comes to high-risk and above areas, Typhoon Maysak accounts for 38% of the total area, while Typhoon Haishen occupies 47%. The regions with low risk are predominantly found in Changchun, across the majority of Baicheng, and at the intersection of Baishan and Jilin. Upon comparing the disasters induced by two typhoons in Jilin Province, it was observed that the disasters caused by Typhoon Maysak were considerably more severe than those caused by Typhoon Haishen. This finding aligns with the intense wind and heavy rainfall brought by Typhoon Maysak.

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A Predictive Model for Estimating Damage from Wind Waves during Coastal Storms

Cited by 3 publications

References 7 publications

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

Machine Learning Models for Storm Damage Prediction in Nova Scotia, Canada

Game Theory-Based Comparison of Disaster Risk Assessment for Two Landfall Typhoons: A Case Study of Jilin Province’s Impact

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