Typhoon Maemi was generated as a tropical cyclone in the sea near Guam on September 4, 2003 and landed on the southern coast of the Korean peninsula on September 11 after passing through Okinawa Prefecture, Japan. The estimated The estimated coefficients from the regression model are then used to estimate ex-ante household vulnera- These values indicate that there is no substantial multicollinearity between variables. Furthermore, the potential risk of natural disaster is the one of the major points used to determine the premium. The building construction companies are also able to improve their design guidelines by planning storm-resistant buildings and by assessing building loss based on the predicted total value of property, construction type, and the number of floors of the building. Typhoon Pablo (Bopha) , 4 December 2012 World’sdeadliest storm in 2012 (strong winds- 260 km per hour and heavy rainfall- 500 mm in 24 hours); Reportedly the most powerful storm to hit southern Mindanao in more than 100 years. Therefore, there is a need for a more comprehensive loss data using the damages associated with various categories of typhoons for development of the vulnerability function in future studies, in order to support the results of this study. Your Health Record could be shared as a Facebook status? The basic building information, e.g., total amount of the property, construction type, number of floors, and number of underground floors, is used as indicators to reveal the typhoon vulnerability according to the building inventory. The city has been affected by recent floods following tropical storm Sendong (Washi, December 2011), typhoon Pablo (Bopha, December 2012) and tropical storm Vinta (Tembin, December 2017). Based on the existing location information, the wind speed and distance from the property centroid to the coastline are estimated. Narisma also cited a study of the United Nations University for Environment and Human Security (UNU-EHS), which revealed that the Philippines ranked third in the 2013 World Risk Index. Therefore, there is an urgent need for metrics and models that can easily and directly estimate and assess the vulnerabilities of the buildings to typhoons in these countries and situations. The indicators can be listed in the descending order of their beta coefficients. © 2020 - Emergency Live. Again as to future research, the values of adjusted R2 were 0.332 for commercial buildings, 0.587 for residential buildings, and 0.403 for industrial buildings, which indicate that the residual variability of the damage is described by some indeterminate indicators. Although several previous studies on economic loss associated with natural catastrophe have identified essential risk indicators, there has been a shortage of more specific research studies focusing on the correlation between vulnerability and economic loss caused by typhoons. Previous studies on wind speed and precipitation in Korean peninsula have been conducted with similar yet various focuses: typhoon risk assessment wind speed from the GIS (Geographic Information System) [25], natural hazard prediction modeling based on a wind speed of typhoon and precipitation [26], characteristics of the damage scale and risk management system by strong wind speed of typhoon [27], damage analysis of meteorological disasters for each district considering the regional characteristics [28], and measuring typhoon damage by wind speed in the rural area properties [29]. It is dangerous to conclude the risk solely with the existing standard models alone. This study also intends to develop and model a local vulnerability function, which assesses the damage of buildings from the results of typhoons by conducting the statistical analysis of the risk factors, in order for many sectors including federal and local governments, insurance companies, and construction companies to utilize in their own damage assessment. Among all the typhoons that have affected the Korean peninsula, it was the most destructive at the time of landing. The construction type also shows a main indicator of the building’s typhoon vulnerability. Probable maximum loss (PML) has to be taken into account by an underwriter when taking risks. Beta coefficient designates standardized coefficients that disregard the unit scale of independent variable, which helps comparisons among the independent variables. These abovementioned studies have similarities with this study in the sense that most of them relied on the past record of financial information and data in measuring the damage and vulnerability from hurricanes/windstorms/typhoons of residential or industrial buildings in their analyses. This means that there is a significant relationship between the dependent and independent variables. The typhoon updated the record in various ways; the central pressure was 910 hPa, the maximum wind speed was 54 m/s, and the maximum size was 460 km (radius). The maximum wind speed and loss due to the typhoon are positively interrelated. Similar to the mentioned studies, this study conducted a vulnerability analysis in order to present the valid risk factors related to building vulnerability based on the accumulated past data and statistics. The answer is no. This signifies that the degree of loss rises as a building is closer to the coast. The construction type is also an important indicator of the building’s typhoon vulnerability. One of the reasons for this is that different insurance companies may show results unlike those from the standardized model due to the diverse business preference, portfolio, and capital. Table 1 shows the distribution of loss per province from Typhoon Maemi. Variables for natural hazard indicators included maximum wind speed and distance from coast. Furthermore, many international organizations, e.g., HAZUS Multi-Hazard (MH) in the United States, the Florida Pubic Hurricane Loss Model (FPHLM) in the state of Florida, the Central American Probability Risk Assessment (CAPRA) in South America, the new Multi-Risk and Multi-Risk Assessment Method (MATRIX) in Europe, and the RiskScape in New Zealand, are investing a lot of resources in developing natural catastrophe models to be able to better predict, respond, and mitigate the risks associated with natural disasters. Recent typhoons have revealed the vulnerability of local communities to climate-related hazards such as severe storms, heavy flooding, rain-induced landslides, and storm surges. Nevertheless, the remaining 59.7% caused by some unproven indicators was not considered in this study. In order to reduce the uncertainty of the evaluation model, the demand for and importance of identifying and developing work through proxy measurements of risks is increasing. The dependent variable value used in the regression model is shown in equation (2): The loss records are consisted of two categories: (1) accident details, e.g., details of the accident, the address, the amount of loss, and the date of the accident, and (2) basic building information, e.g., the total amount of the property, construction type, number of floors, and number of underground floors. Highfield et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The hazard module identifies the frequency and intensity of typhoons and other typhoon information, e.g., storm surge, precipitation, and central pressure, and regenerates typhoons in specific areas and periods according to the prescribed information. The research scope is limited to South Korea. Typhoon Vulnerability Analysis in South Korea Utilizing Damage Record of Typhoon Maemi, Department of Architectural Engineering, Mokpo National University, 1666 Yeongsan-Ro, Cheonggye-myeon, Muan-gun, Jeonnam 58554, Republic of Korea, School of Architectural Engineering, University of Ulsan, 93 Daehak-Ro, Ulsan 44610, Republic of Korea, Department of Civil Engineering and Engineering Mechanics, Columbia University, 500 W. 120th Street #610, New York, NY 10027, USA, http://typ.kma.go.kr/TYPHOON/down/2011/%C0%CE%B8%ED%B9%D7%C0%E7%BB%EA%C7%C7%C7%D8.pdf, K. T. Bhatia, G. A. Vecchi, T. R. Knutson et al., “Recent increases in tropical cyclone intensification rates,”. In this study, a regression analysis was first used to determine the significant loss indicators for building vulnerability and then to evaluate the relationship between the indicators and loss ratio. To be more specific, the monetary data was adopted because of its definiteness and objectivity [30]. DAVAO CITY (MindaNews/05 December)– “We cannot stop a storm but vulnerability is something we can address.” Dr. Gemma Narisma, head of the Regional Climate Systems program of the Manila Observatory, shows the satellite image of super typhoon Yolanda during the fourm with the members of the CODE-NGO in Davao City on Thursday. Furthermore, in developing countries with emerging economies, in which insurance penetration rates are relatively low, it is strongly required to create vulnerability functions using historical loss records. The lower the property value of a building, the more vulnerable it is to typhoons. These countries can assess windstorm losses by adopting the frameworks and indicators used in this study, although the weight of the indicator should be adjusted by the weight of each province and the coefficient to reflect the local building vulnerability for use in other areas. The reason why the significant indicators and adjusted R2 are different among the regression model is that they have different damage vulnerabilities against typhoon damage. Typhoon Pablo has unveiled the vulnerability of our Mindanao communities to typhoons, landslides and flash floods. These values show that there is no noteworthy multicollinearity between variables. That is, based on the data used and the significant factors found in this study, vulnerability curves can be created in a subsequent research. More specifically, it is a problem that models can only be developed and evaluated in a limited number of countries, such as the United States, China, and Japan, which often suffer large losses due to natural disasters and large insurance industries. She called on the need to invest in education or literacy to reduce vulnerability. In addition, all of these studies aimed to suggest a loss function or a model to adopt in an attempt to estimate and simulate the damage and loss in other areas in the event of future hurricanes/windstorms/typhoons. In this study, based on the actual record of damages and the consequent financial losses caused by typhoons documented and accumulated by a major insurance company in Korea, it is first aimed to identify the statistically significant risk factors of buildings and of typhoons while relying on the objectivity and accuracy of the quantitative data. Following the scale of the coefficient, the indicators are (1) the total value of property (beta coefficient = –0.549) and (2) construction type (beta coefficient = 0.241). Keywords: Barangay Risk Reduction and Management Plan, disaster preparedness, response and relief, rehabilitation flood simulations and vulnerability assessment against pluvial flooding and coastal flooding due to storm surge during the passage of Typhoon Pablo. “It showed during Pablo, it showed during Yolanda. 1 Map of sites used as evacuation shelters within Cagayan De Oro in the Philippines during 3 recent flood events caused by tropical storms Sendong (2011), Pablo (2012) and Vinta (2017). iii Shelter 227,953,000.00 271,980,000.00 Health (WASH) - 30,746,800.00TABLE OF CONTENTS INTRODUCTION Hazard Assessment 1 Typhoon Pablo 2 Vulnerability and Exposure 2 B. Robert et al., U. Ulbrich, A. H. Fink, M. Klawa, and J. G. Pinto, “Three extreme storms over Europe in December 1999,”, E. E. Koks, B. Jongman, T. G. Husby, and W. J. W. Botzen, “Combining hazard, exposure and social vulnerability to provide lessons for flood risk management,”, A. C. Khanduri and G. C. Morrow, “Vulnerability of buildings to windstorms and insurance loss estimation,”, J.-M. Kim, T. Kim, and K. Son, “Revealing building vulnerability to windstorms through an insurance claim payout prediction model: a case study in South Korea,”, H. Ryu, K. Son, and J.-M. Kim, “Loss prediction model for building construction projects using insurance claim payout,”. In addition to high winds up to 185 kph, intense heavy rainfall and storm surges, Typhoon Pablo also generated a massive debris flow in the municipality of New Bataan [5]. Therefore, findings and results of this research can serve as references and provide vital directions to abovementioned sectors such as federal and local governments, insurance companies, and construction companies in predicting typhoon losses. In the regression analysis of the commercial building, the adjusted R2 value is 0.332, which indicates that 33.2% of the variant of the loss ratio can be described by the regression model. Among the key indicators of the variables, the value of property is the significant indicator that is shared among the three models. However, the other indicators are not associated with the loss ratio of residential building. We are committed to sharing findings related to COVID-19 as quickly as possible. Meanwhile, claim payout records of insurance companies can provide specific, accurate, and reliable loss data. Gyeongnam was damaged by the typhoon to the dollar amount of loss (48.0%) and the number of losses (35.4%). As a result, the LOB category can classify buildings as physical and financial functions. Based on the field observations and forensic interpretations, this study presents fragility functions obtained from damage statistics for wattle and daub houses [24]. B. Kruse, Y. Wang, and Y. Wang, “Spatial dependencies in wind-related housing damage,”, J.-M. Kim, K. Son, Y. Yoo, D. Lee, and D. Kim, “Identifying risk indicators of building damage due to typhoons: focusing on cases of South Korea,”, P. J. Vickery, P. F. Skerlj, J. Lin, L. A. Twisdale Jr, M. A. The finance module estimates the economic losses based on the policy term, e.g., risk excess of loss, cat excess of loss, and limit of liability. They should be able to judge whether the outcome is optimistic, pessimistic, or conservative, depending on their exposure. The typhoon landed on the south coast on the Korean peninsula, through the inland, to the east coast, causing extreme economic losses in many cities on the south coast, as shown in Figure 1(b). Nevertheless, the other indicators are not related with the dependent variable of residential building. She called on the need to invest in education or literacy to reduce vulnerability. 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