• Journal of the Society of Disaster Information
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• v.11 no.2
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• pp.203-209
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• 2015

The heat detector detects heat in the fire and is an important core element of the automatic fire alarm system used generally for every fire prevention objects. The heat detector is distinguished in spot type and spread type and in spot type, there are differential and thermistor types. These heat detectors give a great influence on the loss of people and property according to the sensitivity of response such as operation time and operation temperature in actual fire and in overseas people apply it for the development of products that can be operated in the early stage of fire including certification, quality management, and comparison standard by introducing response time index through the theory of heat balance that considers the heat loss and ventilation tests. In Korea, the response time index is introduced and used in the head of sprinkler products, but it is not applied to the heat detector at present. It is necessary to introduce the response time index that shows the sensitivity of response of the heat detector the installation standard for the heat detector that the response time index is applied should be different according to the fire weight, danger degree of fire, and shape of buildings. Through this study, it tries to help reduce lives and property of people through the swift warning by installing detectors suitable for the building structure.

• 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.