Development of real-time forest fire spread prediction model based on big data

<p>Spatiotemporal prediction of wildfire spread is very important to minimize damage and respond urgently to these urban forest fires since forest fire damages caused by strong winds such as the Foehn wind are increasing every year, especially along the eastern coastal cities in Korea. Because forest fires spread under the influence of environmental factors such as fuel, topography, and weather, the values of these factors are known as important variables for accurate forest fire spread prediction models. In this study, we developed a forest fire spread prediction model that considers wind speed, wind direction, fuel information, and slope as main factors by analyzing past forest fire damage data in Gangwon-do such as meteorological factors, fuel and terrain characteristics. The wildfire spread prediction model (hereinafter referred to as WINS, Wind field Network for Fire Spread Simulation) produces meteorological information of a numerical forecasting model calibrated with MOS (Model output statistics) as 1km x 1km grid values, and the slope and fuel information between each grid are configured. Land use information in the Gangwon area is divided into artificial grassland, mixed forest, natural measures, coniferous forest, and broad-leaved forest, and the depth of the surface fuel layer and the amount of water removal surface fuel are layered by grid according to Anderson fuel type. As soon as the ignition point information is obtained, the predicted wind speed and wind direction values of the grid are layered by time and GIS-based predicted spatiotemporal information is produced. The WINS model for forest fire cases in the Gangwon region occurred from 2019 to 2021 was verified, and real-time map-based forest fire spread prediction information was utilized by local governments and related stakeholders in the urban forest fire response task and decision-making stage according to the simulated scenario.</p> <p>&#160;</p> <p>"This research was supported by the program of Research Program to Solve Urgent Safety Issues (2022M3E9A1095664), through the National Research Foundation of Korea(NRF), funded by the Korean government. (Ministry of Science and ICT(MSIT), Ministry of the Interior and Safety(MOIS))."</p>