Establishment of Hazard-triggering Rainfall According to Heavy Rain Damage Scale: Focused on Gyeonggi-do

Kim, Jongsung; 토목공학과, 인하대학교; Choi, Changhyun; Kim, Dong‐Hyun; Joo, Hongjun; Kim, Jung-Wook; Kim, Hung Soo

doi:10.14383/cri.2018.13.4.297

Cited by 3 publications

(3 citation statements)

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“…자연재해로 인한 피해에 대비하기 위한 연구가 활발히 이루어졌으며, 특히 피해 발생 여부와 피해액 추정을 위한 여러 모형이 제시되었다. 자연재해 원인별로 재해를 유발하 는 한계치(triggle or threshold)를 설정하여 재해 발생 여부를 판정하기 위한 연구로 Kim et al (2018) 재해 유발 한계치와 관련된 가장 활발한 연구 분야 중 하나는 산사태를 예측하기 위한 연구로, Guzzetti et al (2007), Brunetti et al (2010)은 Bayesian inference method, Frequentist method를 이용하여 강우 한계치를 강우지속기간과 강우강 도의 관계로 설명하였고, 국내에서는 Choi (1989)에 의하여 연속강우량과 시간강우강도 개념을 이용하여 산사태 발생 을 설명한 이후로 Hong et al (1990), Lee (1991), Kim et al (1998), Kim and Jung (2000), Yang et al (2007), Yun et al (2010) 등에 의하여 강우강도 또는 특정 강우지속기간 동안의 강우량을 이용한 강우 한계치에 대한 연구가 이루어 졌다 (Ham and Hwang, 2014에서 재인용). Ham and Hwang (2014)…”

Section: 서 론unclassified

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Kim

2023

J. Korean Soc. Hazard Mitig

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Typhoon damage in coastal areas is a complex disaster characterized by heavy rain, strong winds, and turbulent waves. The strength of these strong winds and waves is determined by the intensity of the typhoon. In this study, a variable representing strong winds and waves was developed. A damage prediction model using rainfall, wind, and the developed variable was then proposed and thresholds for each variable were calculated across cities, counties, and districts located in coastal areas. Two damage prediction models were compared based on variables. Standard normal variables related to strong winds and rainfall by duration were calculated using Empirical Copula. The first logistic regression model was constructed using these variables. The second model used a multiple logistic regression model, considering strong winds and rainfall by duration. Although there are more cities, counties, and districts where the multiple logistic regression model exhibited better accuracy, there have been several instances where the typhoon damage induction threshold suggested by this model was abnormal. On the other hand, a relatively stable critical value was yielded using standard normal variables by Empirical Copula. This is because standard normal variables are calculated based on the probability according to the order of rainfall and strong wind magnitude. Hence, the critical value falls within the minimum and maximum range of observed values. According to the research results, it is possible to predict whether typhoon damage will occur in cities, counties, and districts located in coastal areas using the typhoon information provided by the Korea Meteorological Administration during a typhoon event, which can aid in preparing for typhoon disasters.

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Section: 서 론unclassified

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Kim

2023

J. Korean Soc. Hazard Mitig

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“…There are statistical methods of rainfall ensemble and the relationship of IDF (Intensity-Duration-Frequency) curve and the return period for determination of critical rainfall (Kim et al, 2011;Bezak et al, 2015;Yang et al, 2016). The critical rainfall for improvement of Heavy Rain Advisory (HRA) or Heavy Rain Warning (HRW) is determined by the occurrence probability of damage in each rainfall duration obtained by Bayesian theory (Park et al, 2014;Montesarchio et al, 2015;Song et al, 2016;Cho et al, 2018;Jeong et al, 2017;Lopez et al, 2017;Choi et al, 2018;Lee et al, 2018;Kim et al, 2018). The previous studies have mostly suggested the criteria based on the specific rainfall intensity and this can explain for the localized heavy rainall but it is difficult to explain for relatively small amount of rainfall having long duration.…”

Section: Introductionmentioning

confidence: 99%

Determination of Heavy Rain Damage-Triggering Rainfall Criteria Based on Data Mining

Kim

Choi

et al. 2020

Preprint

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Abstract. Heavy rainfall occurs over the Korean peninsula mainly because of typhoons and a localized heavy rainfall, leading to severe flooding and landslide risk. KMA (Korean Meteorological Administration) has the criteria for issuing a Heavy Rain Advisory (HRA) over the peninsula even though each region or local government has different conditions in capability of disaster prevention (CDP) and different characteristics in rainfall and heavy rain damage. Therefore, the aim of this study is to suggest the methodology for the determination of Heavy rain Damage-Triggering Rainfall Criteria (HD-TRC) that HRA can be issued in each region. The study regions are local governments in Gyeonggi-province, Seoul-city, and Incheon-city in Korea. HD-TRC can be determined based on rainfall and heavy rain damage data. The data from 2005 to 2018 are collected and then the data for flood or rainy season from June to September are extracted. The rainfall data is provided in KMA and heavy rain damage data during disaster periods (DPs) can be obtained from the statistical yearbook of natural disaster (SYND) published by MOIS (Minstry of Interior and Safety) every year. Training set of 2005 to 2014 is used for obtaining HD-TRC and test set of 2015 to 2018 is used for evaluating three criteria of HD-TRC, Advanced HD-TRC, and HRA. Analysis for determining the best criteria is performed through data mining processes as follows: (1) Maximum rainfalls in durations of 1 to 24-hr (X1) and antecedent rainfalls of 1 to 7-day (X2) are obtained and used as independent variables. Heavy rain damage data are divided into damage day (1) and no damage day (0) used as dependent variables (Y). Principal component analysis (PCA) is performed and PCs (principal components) are obtained as PC.X1 and PC.X2 for independent variables. Then Risk Index (RI) is defined as PC.X1 + PC.X2 and RIs become the candidates for HD-TRC. The predicted damage (Ŷ) is obtained based on RIs and confusion matrix is constructed then the best HD-TRC is determined through the evaluation of classification performance. (2) However, ‘abnormal days’ (ADs) in a DP that the damage is occurred exists. The ADs mean the days which we do not have rainfall or have small rainfall amount during DP. Say, ADs have too small rainfall to damage even during DP. The ADs are defined as days below rainfall of 20 mm and 5 cases of ADs are also defined as 0, 0–5, 0–10, 0–15, and 0–20 mm in this study. We count total days in all the DPs and in ADs for a case. The ratio of ADs to total days during DPs could be the occurrence probability or prior probability (PP) of ADs for a case and 5 PPs are obtained. Also, the average AD for each case can be obtained and defined as risk range (RR). Then we define Advanced HD-TRC using MCS (Monte Carlo Simulation) linked with PP, RR, and from HD-TRC for each case. Therefore, HD-TRC is determined based on RI and Advanced HD-TRC for each case based on PP and RR. Finally, three criteria of HD-TRC, Advanced HD-TRC, and HRA are compared based on performance evaluation by test set. As a result, Advanced HD-TRC shows the best performance and so the suggested methodology can be used for regional heavy rain damage warning information.

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“…즉, 이와 같은 피해 금액을 예측하기 위해서는 선행적으로 피해 발생 여부 를 판단할 수 있는 모형을 추가적으로 고려할 필요가 있다. 자연재해 발생 여부를 판단하기 위한 연구를 살펴보면 피해자료와 기상정보를 연계하여 피해가 발생하는 임계 기준을 설정하는 연구가 주를 이루었다 (Montesarchio et al, 2009;Park and Kang, 2014;Song et al, 2016;Jeong et al, 2017;Cho et al, 2018;Kim et al, 2018;Lee et al, 2018) (Dai and Lee, 2002). 로지스틱 회귀 모형은 독립변수에 대한 선형식으로 다음과 같이 표현할 수 있다.…”

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Developing a Prediction Model (Heavy Rain Damage Occurrence Probability) Based on Machine Learning

Kim¹,

Lee²,

Kim³

et al. 2019

J. Korean Soc. Hazard Mitig

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In this study, a prediction model (heavy rain damage occurrence probability or PM-HDOP) was developed for a metropolitan area. The heavy rain damage and rainfall data were collected as dependent and independent variables, respectively. The dataset was divided into training (2005-2016) and test sections (2017). We developed the PM-HDOP using machine learning methods such as logistic regression, artificial neural network, bagging, random forest, and boosting to predict the occurrence of nonlinear natural disasters. An architectural model with the best performance was selected, and the PM-HDOP was subsequently used to predict the probability of occurrence. As a result, a boosting scheme showed the best performance in Gyeonggi-do and Seoul, and a bagging scheme showed the best performance in Incheon. If the results of this study are used to predict the occurrence of heavy rain damage, which is not currently being serviced in Korea, it is possible to effectively reduce the damages.

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Establishment of Hazard-triggering Rainfall According to Heavy Rain Damage Scale: Focused on Gyeonggi-do

Cited by 3 publications

References 0 publications

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Determination of Heavy Rain Damage-Triggering Rainfall Criteria Based on Data Mining

Developing a Prediction Model (Heavy Rain Damage Occurrence Probability) Based on Machine Learning

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