Jae-Joon Lee

This study investigated the effects of wind-speed input conditions on FlamMap’s minimum travel time (MTT) simulation results and its applicability by evaluating multiple wind-speed multiplier scenarios based on a station-based representative wind speed for the 2025 Uiseong wildfire in Gyeongsangbuk-do, Republic of Korea. Representative wind conditions were derived from nearby weather stations using Inverse Distance Weighting (IDW), and nine wind-speed scenarios ranging from 1.0 to 5.0 times the representative wind speed were established at 0.5 intervals. Comparison with reference data derived from NASA Fire Information for Resource Management System (FIRMS) active fire detections indicated that, across all analysis times, the 5.0-times condition achieved the highest mean intersection over union (IoU) and the lowest area mean absolute error (MAE), whereas the 4.5-times condition recorded the lowest mean absolute percentage error (MAPE) of the maximum straight-line spread distance (distance MAPE). During the early spread period of 180–300 min, the 5.0-times condition yielded the highest mean IoU, whereas the 4.0-times condition produced the lowest distance MAPE. These results indicate that the most suitable wind-speed condition varied according to the evaluation metric and analysis period. The findings of this study are limited to the Uiseong wildfire case and the applied input conditions and may serve as baseline information for developing wind-speed scenarios for future wildfire risk assessments.

This study quantified toxic impact ranges resulting from a liquefied ammonia release when ammonia is used as a fuel in combined cycle power plants and examined how ground roughness and site-specific meteorology alter those ranges. Two sites operated by Korea Midland Power were selected: the Taean site, representing a coastal environment (Open Country roughness), and the Incheon site, representing an urban-industrial environment (Urban or Forest roughness). An identical virtual horizontal cylindrical storage tank (2 m diameter, 4 m length, liquid ammonia, 20 °C, 84% fill) was assumed for both sites so that only environmental conditions differed. A worst-case scenario (8 cm hole, conservative meteorology: 1.5 m/s wind, stability class F, 25 °C, 50% relative humidity) and an alternative scenario (1.6 cm hole, 2025 annual-mean meteorology from the nearest observatory, stability class D) were simulated in Areal Locations of Hazardous Atmospheres (ALOHA). Under the worst-case scenario, the ERPG-3/ERPG-2/ERPG-1 distances were 1.77/4.02/7.56 km at Taean and 1.41/3.22/6.12 km at Incheon; under identical release and weather conditions, the smoother coastal terrain produced an ERPG-2 distance 0.80 km larger. Under the alternative scenario, the corresponding distances were 0.35/1.28/3.22 km at Taean and 0.31/1.38/4.02 km at Incheon. These results indicate that toxic impact distances for ammonia releases are governed by the combined effects of ground roughness, hole size, wind speed, wind direction, temperature, and humidity. Consequence analysis for ammonia-fueled power plants should therefore consider both conservative and realistic meteorological bases, and ERPG-2 distances should be interpreted together with the surrounding land use when emergency response and evacuation plans are prepared.

In this study, we aimed to analyze the dispersion characteristics of potential chlorine leakage incidents at the Yeosu Industrial Complex with a focus on seasonal meteorological conditions and variations in the release volume. To understand how damage patterns vary by season even under identical leakage conditions, summer and winter were selected as representative scenarios wherein temperature, atmospheric stability, and wind-speed differ markedly. The Areal Locations of Hazardous Atmospheres program was used for the dispersion analysis, which employed the average meteorological data from the preceding 10 years (2013-2022) and chlorine storage quantities specified by the Industrial Safety and Health Act as the basis for 50%, 100%, and 150% leak conditions. The Acute Exposure Guideline Level (AEGL) criteria were employed to calculate the impact range by concentration level, and the indoor and outdoor exposure times were compared. The simulation results indicated that during the winter months, stable atmospheric conditions and low temperatures resulted in the widest dispersion range; during the summer months, high-concentration zones formed near the leakage point, and the overall dispersion distance was relatively limited. Outdoor concentrations attained the AEGL-2 standard within minutes whereas, in indoor environments, even at equivalent distances, the time to reach the standard was delayed by tens of minutes, and thereby confirmed an initial exposure-mitigation effect. By quantitatively quantifying temporal and spatial variations in the exposure risk through an integrated consideration of seasonal weather conditions and indoor/outdoor environments, this study improves upon previous process safety management-based assessments that are centered on fixed worst-case scenarios.

In this study, we quantitatively analyze the explosion and toxic dispersion ranges of three common isomers of xylene (o-, m-, and p-xylene) that are widely used in industrial applications with regard to variations in their physicochemical properties. Previous studies on chemical accident risks could not fully consider subtle differences among xylene isomers because they treated xylene as a single substance. We address this limitation by providing foundational data to develop more precise chemical accident response measures. The dispersion of hazardous substances was simulated using a hazard modeling program provided by the US Environmental Protection Agency called ALOHA to analyze the characteristics of each isomer. Explosion and toxic release scenarios were designed with the boiling points and vapor pressures of each isomer as key property values. The differences among the isomers were compared by calculating the overpressure damage range in an explosion scenario and the toxic dispersion distance in a release scenario. The results of the analysis show that all three isomers exhibited similar overpressure damage ranges in the explosion scenario. This indicates that subtle differences in properties such as boiling point and vapor pressure are not fully reflected in explosion energy because an explosion is an instantaneous reaction that occurs within a short period of time. In the release scenario, however, m-xylene exhibited a wider toxic dispersion range than the other isomers. This is attributed to the relatively high vapor pressure and rate of vaporization, which indicates that additional safety management measures are required while handling m-xylene. Our findings highlight that the physicochemical properties of each isomer should be considered beyond the existing practice of treating xylene as a single substance in established chemical accident response plans. These results also contribute to a more precise prediction of accidents and to the development of safety management strategies.

Leakage of flammable gases and the resulting explosions in petrochemical plants remain latent risks, capable of occurring at any moment. Therefore, to address these worst-case scenarios within a virtual reality framework, we conducted simulations aimed at predicting and effectively responding to potential damages due to gas leakage. This study presents an analysis of the hazards that can lead to leaks and potential explosions in a petrochemical plant using the BREEZE Incident Analyst program. Rapid and accurate recognition of the risk associated with gas leaks, which can cause extensive damage and explosions, is of paramount importance. This study addresses two main aspects: the prediction of the consequences of gas leaks through simulations and the implementation of appropriate detection measures. Better, more efficient risk management and mitigation strategies were implemented by predicting gas leak paths using BREEZE. Using ultrasonic detection technology, detection was demonstrated to be possible in approximately one-third the time required by conventional detectors, and it is weather-insensitive. Simultaneously, considering plant characteristics such as utility configurations, we propose an additional method to prevent leaks from going undetected. This is achieved by integrating gas detection technology that combines both existing and new technologies.

Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.

Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.

Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.

Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.

Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.