## 1 Introduction  
Forest fires have a profound impact on ecological processes on the Earth, such as deforestation, habitat destruction, and loss of soil nutrients, as well as the environmental, economic, and social sectors [1; 2; 3]. It takes a long time and significant effort to restore areas with forest fires to their previous state. Therefore, it is necessary to make efforts to reduce the damage caused by forest fires. One such effort is to identify areas with high forest fire risk in advance to establish proper mitigation and response plans from a management perspective. In recent years, many studies have also tried to predict where a forest fire is likely to occur [1; 4; 5; 6]. Anthropogenic, topographic, meteorological, and fuel factors are mainly used to calculate fire risk [7; 8; 9]. The anthropogenic factors, such as distance from roads, distance from rivers, and urban areas, and topographic factors, such as elevation and slope, have been used for accessibility consideration [10; 11]. As meteorological data, temperature, relative humidity, precipitation, and wind speed have been mainly used to consider dry weather conditions prone to fires. These meteorological variables have been typically extracted by interpolating station values or from numerical model data [7; 12]. Some studies usually utilized satellite-derived indices to extract fire-prone dry fuel or weather conditions to explain the relationship between input factors and forest fires. However, there are limitations due to cloud contamination and coarse spatial or temporal resolution when using optical satellite sensor systems [4]. To predict forest fire susceptibility and risk, a lot of efforts have been made worldwide. The Canadian Forestry Service has developed the Canadian Forest Fire Weather Index (CFWI) and has been officially conducting forest fire risk forecasting since 1970. CFWI has been widely studied for its feasibility in many countries including Australia, the United Kingdom, Greece, China, and South Korea [5; 13; 14; 15; 16; 17]. The Met Office Fire Severity Index (MOSPI) forest fire risk forecasting system provided by the UK is also based on CFWI [18]. CFWI is a weather index that combines fine fuel moisture code (FFMC), duff moisture code (DMC), drought code (DC), initial spread index (ISI), and build up index (BUI) information obtained using weather information such as temperature, relative humidity, wind speed, and precipitation [19]. CFWI has complicated calculation processes and empirical standards (i.e., coefficients for formula), and thus may not fit well for other areas with different weather conditions, requiring additional careful calibration [20; 21; 22]. The Korea Forest Service's National Forest Research Institute (NFRI) uses weather data--temperature, relative humidity, and wind speed--to calculate real-time Daily Weather Index (DWI) based on logistic regression [23]. NFRI provides DWI showing the degree of forest fire risk by administrative units. DWI is classified into four fire risk classes (low, moderate, high, and very high), providing real-time and 72-h forecasts at the 3-h interval [23; 24]. DWI is mainly focused on weather factors without consideration of other accessibility or surface factors. Thus, it might not work well to predict the susceptibility of forest fires that frequently occur due to anthropogenic factors. In particular, the forest fires caused by anthropogenic factors such as incineration of paddy fields, waste incineration, and cigarettes exceed 70% according to historical data from 2010 to 2019 in South Korea. According to [PERSON] et al. [25], human activities and accessibility in South Korea are closely related to the probability of forest fire occurrence. Thus, it is necessary to develop a forest fire risk monitoring model that considers not only weather conditions but also topographic and anthropogenic factors. In order to simultaneously consider weather, fuel, topography, and anthropogenic factors, it is advantageous to use machine learning.