• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.311-318
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• 1997

On April 27,1993, a forest fire occurred in Morito-area, Manba-city, Gunma-prefecture Japan. Under the prevailing strong winds, the fire spread and extended to the largest scale ever in Gunma-prefecture. The author chartered a helicopter on May 5, one week after the fire was extinguished, and took aerial photos of tile damaged area, and investigated the condition. of the fire through field survey and data collection. The burnt area extended. over about 100 hectares, and the damage amounted to about 190 million yen (about two million dollar). The fire occurred at a steep mountainous area and under strong winds, therefore, md and topography strongly facilitated the spreading, It is the purpose of this paper to report a damage investigation of the fire and to develop the forecasting method of forest fires based on the topographical analysis and spreading speed of fire. In the first place, I analyze the topographical structure of the regions which became the bject of this study with some topographical factors, and construct a land form classification ap. Secondly, I decide the dangerous condition of each region in the land form classification map according to the direction of the wind and spreading speed of f'kre. In the present paper, I try to forecast forest fires in Morito area, and the basic results for the forecasting method of forest fires were obtained with the topographical classification system and spreading speed of fire.

• Fire Science and Engineering
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• v.22 no.5
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• pp.112-124
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• 2008

The extra session of the 17th National Assembly convened lastly had passed the law on the promotion of the fire industry. Therefore, the development of the fire industry discussed insignificantly in accordance with the establishment of the law. In this important time, this study was intended to derive tasks for development through researching the concept and developing process of the fire industry and forecasting the industry. For defining the concept of the fire industry, this study researched meaning, the present status and features of the industry. For study of developing process of the fire industry, this study analysed fire law and system, fire codes, fire products and development of fire market. The analyses were processed as a unit of every 10 years from 1960's. Consequently, this study presented some tasks for development of the fire industry such as fire inspection system, fire products circulation system, separated ordering system, managing system of fire facilities and technical R & D of the fire industry based on analysing and forecasting the fire industry.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.56-65
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• 2020

Forecasting of coming fire accidents is quite a challenging problem cause normally fire accidents occur for a variety of reasons and seem randomness. However, if fire accidents that cause critical losses can be forecasted, it can expect to minimize losses through preemptive action. Classifications using machine learning were determined as appropriate classification criteria for the forecasting cause it classified as a constant damage scale and proportion. In addition, the analysis of the periodicity of a critical fire accident showed a certain pattern, but showed a high deviation. So it seems possible to forecast critical fire accidents using advanced prediction techniques rather than simple prediction techniques.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.221-235
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• 2009

This manuscript illustrates the comparative study between ARIMA and Exponential Smoothing modeling to develop forest fire forecasting system using different weather parameters. In this paper, authors have developed the most suitable and closest forecasting models like ARIMA and Exponential Smoothing techniques using different weather parameters. Authors have considered the extremes of the Wind speed, Radiation, Maximum Temperature and Deviation Temperature of the Summer Season form March to June month for the Ranchi Region in Jharkhand. The data is taken by own resource with the help of Automatic Weather Station. This paper consists a deep study of the effect of extreme values of the different parameters on the weather fluctuations which creates forest fires in the region. In this paper, the numerical illustration has been incorporated to support the present study. Comparative study of different suitable models also incorporated and best fitted model has been tested for these parameters.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.146-156
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• 2016

In this study, we developed a large fire forecasting system using critical weather conditions, such as strong winds and effective humidity. We incorporated information on forest type prior to large fires using an incident case study. The case study includes thirty-seven large fires covering more than 100 ha of damaged area over the last 20 years. Dangerous large fire regions were identified as areas of more than 30 ha of Korean red pine and the surrounding two kilometers. Large fires occur when wind speeds average 5.3 m/s with a maximum of 11.6 m/s and standard deviation of 2.5 m/s. Effective humidity for large fires average 30% with a minimum of 13% and standard deviation of 14.5%. In dangerous Korean red pine stand areas, the large fire 'Watch' level is issued when effective humidity is 30-45% for more than two days and average wind speed is 7-10 m/s. The 'Warning' level is issued when effective humidity is less than 30% for more than two days and average wind speed is more than 11 m/s. Therefore, from now on, the large fire forecasting system can be used effectively for forest fire prevention activities based on a selection and concentration strategy in dangerous large fire regions using severe weather conditions.

• Journal of the Korean Institute of Gas
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• v.19 no.2
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• pp.20-28
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• 2015

A suitable monitoring method is necessary for successful policy implementation and its evaluation, required for effective prevention of abnormal fire occurrences. To do this, there were studies for applying control charts of quality management to fire occurrence monitoring. As a result, it was proved that more fire occurs in winter and its trend moves yearly-basis with some patterns. Although it has trend, if we apply the same criteria for each time, inefficient overreacting fire prevention policy will be accomplished in winter, and deficient policy will be accomplished in summer. Thus, applying different control limits adaptively for each time would enable better forecasting and monitoring of fire occurrences. In this study, we treat fire occurrences as time series model and propose a method for configuring its coefficients with ARIMA model. Based on this, we expect to carry out advanced analysis of fire occurrences and reasonable implementation of prevention activities.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.29-35
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• 2011

A forest fire was one of the huge disasters and damaged human lifes and a properties. Therefore, many countries operated forest fire forecasting systems which developed from forest fire records, weather data, fuel models and etc. And many countries also estimated future state of forest fire using a long-term climate forecasting like GCMs and prepared resources for future huge disasters. In this study, we analyzed relationships between forest fire occurrence and meteorological factors (the minimum temperature ($^{\circ}C$), the relative humidity (%), the precipitation (mm), the duration of sunshine (hour) and etc.) for developing a estimating tools, which could forecast forest fire regime under future climate change condition. Results showed that forest fires in this area were mainly occurred when the maximum temperature was $10{\sim}200^{\circ}C$, when the relative humidity was 40~60%, and when the average wind speed was under 2m/s. And forest fires mainly occurred at 2~3 day after rainfall.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• Journal of the Korea Society for Simulation
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• v.8 no.2
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• pp.13-28
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• 1999

The main purpose of this paper is to design and implement forest fire information system (FOFIS) for effective prevention of forest fire using GIS, database, 3-D graphics, and simulation techniques. In contrast to conventional fire information systems that are mostly based on the 2-D graphics and analytic modeling approaches, we have proposed the cell-based modeling approaches, i.e., spatial, data, and simulation modeling approaches. The cell-based spatial modeling is proposed by eliminating the cliff effect of the typical elevation model so that it can provide realistic 3-D graphics of the forest fire. The cell-based data modeling of geography, meteorology, and forestry information is also proposed. The cell-based dynamic modeling for forecasting of the fire diffusion is developed using the variable structure modeling techniques. Several simulation tests of FOFIS performed on a sample forest area of Chungdo, Kyungsangbukdo will demonstrate our approaches.