• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.60-67
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• 2021

Purpose: Traditional markets often have irregular space utilization patterns because the spaces are created and divided as time passed. Internally, there is high risk of fire due to problems such as aging facilities and high-density of stores and externally, there is high risk of fire spread since it is often adjacent to deteriorated residential and commercial facilities. Method: In this paper, on-site investigations were carried out to check fire risk factors and fire spread risk, and fire occurrence and expansion risk factors were investigated for traditional markets in Hwanghak-dong and Dong-daemun by using large-scale fire data from existing traditional markets. Result: As a result of the analysis, there are likely to be various problems such as high fire load and lack of safety awareness due to aging facilities and high-density of stores. In particular, it is necessary to prepare countermeasures because deteriorated residential facilities with narrow alleys around traditional markets have high fire spread. It is situation that while traditional markets mainly are managing for fire and disaster centering on the merchant association, the surrounding residential areas are not properly managed. Conclusion: It is necessary to manage deteriorated residential facilities with traditional markets, also to be linked early warning system and information to evacuate rapidly in case of fire there.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.76-84
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• 2013

An adequate downscaling of the official forecasts of Korea Meteorological Administration (KMA) is a prerequisite to improving the value and utility of agrometeorological information in rural areas, where complex terrain and small farms constitute major features of the landscape. In this study, we suggest a simple correction scheme for scaling down the KMA temperature forecasts from mesoscale (5 km by 5 km) to the local scale (30 m by 30 m) across a rural catchment, especially under temperature inversion conditions. The study area is a rural catchment of $50km^2$ area with complex terrain and located on a southern slope of Mountain Jiri National Park. Temperature forecasts for 0600 LST on 62 days with temperature inversion were selected from the fall 2011-spring 2012 KMA data archive. A geospatial correction scheme which can simulate both cold air drainage and the so-called 'thermal belt' was used to derive the site-specific temperature deviation across the study area at a 30 m by 30 m resolution from the original 5 km by 5 km forecast grids. The observed temperature data at 12 validation sites within the study area showed a substantial reduction in forecast error: from ${\pm}2^{\circ}C$ to ${\pm}1^{\circ}C$ in the mean error range and from $1.9^{\circ}C$ to $1.6^{\circ}C$ in the root mean square error. Improvement was most remarkable at low lying locations showing frequent cold pooling events. Temperature prediction error was less than $2^{\circ}C$ for more than 80% of the observed inversion cases and less than $1^{\circ}C$ for half of the cases. Temperature forecasts corrected by this scheme may accelerate implementation of the freeze and frost early warning service for major fruits growing regions in Korea.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.