## 4 Data Collection and Description  
Figure 1 shows the overall data collection process. This study used data on railroad accidents from 2001 - 2021 maintained by the Railway Safety Information System (RSIS) of the Ministry of Land, Infrastructure and Transport. The RSIS was established in 2012 as a comprehensive safety information system for railway-related organizations, and it keeps a database of railway accidents and obstacles along with railway safety and railway accident management [41]. The data collected include accident details, such as date and time, accident agency, railway classification, accident type, cause, operation failure type, train type, accident location, human casualties (dead, injured, minor injuries, etc.), loss amount, and delay time. In total, data on 14,464 accidents were collected, and 2020 accidents related to natural disasters and weather were extracted according to the accident type and cause.  
To check the loss distribution and identify the cause of loss, the number (frequency) and size (severity) of loss were analyzed. Figure 2 shows the distribution of weather-related accident loss by year. There is a very large difference in the distribution of loss every year, but as can be seen from the linear trend lines, both the amount of loss and the number of loss cases are increasing every year. These demonstrate that the risk of railway accidents caused by weather is growing every year. Figure 3 displays the distribution of weather-related accidents by cause. Causes of loss included snowfall, rainfall, thunderstroke, gale, extreme cold, landslide, and scorching heat. The number of losses appeared in the order of snowfall, rainfall, thunderstroke, and gale, but the amount of loss looked in the order of rainfall,  
Figure 1: Workflow of data collection.  
snowfall, landslide, and scorching heat. In particular, rainfall was found to account for the largest proportion of damage.  
Figure 4 displays the frequency distribution of the input and output variables. Table 1 describes the variables. From the collected data, the amount of weather-induced railroad accident loss was selected as an output variable. The weather information relevant to each railway accident, which was taken from the meteorological data open portal of the Korea Meteorological Administration, was used as the input variable to collect detailed and objective weather data. Since 1904, the Korea Meteorological Administration's meteorological data open portal has provided continuously tracked information on various weather elements (e.g., precipitation, solar radiation, snow, temperature, wind, humidity, temperature). The weather information at the time of each accident was collected based on the date and location of the accident. Weather information from the day of the accident was collected from the meteorological observation point closest to the accident location. Weather information (e.g., wind speed, temperature, rainfall, sunshine, relative humidity, snowfall, fog) on the day of the accident was collected by the Korea Meteorological Administration.  
Figure 3: Distribution of loss by weather events.  
Figure 2: Distribution of loss by year.  
Moreover, as the collected data used various scales, the distributions of both the input and output data were standardized with a mean of 0 and a standard deviation of 1 through the z-score normalization method; this data normalization made it possible to compare data with various scales. All data are composed of numerical variables, and Table 2 provides the descriptive statistics of the collected training set and test set.  
Figure 4: The distribution of the input and output variables.