## 2 Literature Review
### Importance of Predicting Weather-Caused Railway Accident Damage  
Studies are actively being conducted in attempts to quantify railway accidents by using various analysis techniques or analyzing the causal relationship between weather and railway accidents. For instance, [PERSON] et al. used Zero-truncated Negative Binomial Regression and Artificial Neural Network models to predict railroad accidents on South Korea's National Railroad. That study analyzed historical accident data and presented the performance of those models in terms of accurately predicting accidents, thus providing insights into accident prevention [46]. [PERSON] et al. proposed an ensemble learning approach for railway accident prediction that combined multiple models. That study involved both data collection and the analysis of historical accident records. The research results demonstrate the effectiveness of the ensemble strategy in enhancing prediction accuracy and its potential applicability in railway safety management [47]. In a different study, [PERSON] et al. ranked the risk factors contributing to financial losses from railroad incidents using machine learning. That research analyzed incident and financial data to identify factors that significantly affect losses. The results provide valuable guidance for risk assessment and mitigation strategies in the railroad industry [48]. [PERSON] et al. integrated Rough Set and Fuzzy Approximation Space with Deep Learning for improved precipitation prediction. That study involved preprocessing weather data and implementing the integrated model. The results showed that this approach achieved enhanced accuracy, thus contributing to more reliable precipitation forecasts [49]. [PERSON] et al. designed a wind hazard warning system for high-speed trains that involved wind data analysis and system implementation. That research showcased the system's effectiveness in detecting and warning against potential wind hazards, thereby contributing to safe and efficient high-speed train operations [50]. [PERSON] et al. proposed a natural disaster warning system for high-speed railways that leveraged disaster prediction technologies. That study showed that the system efficiently provided timely warnings during natural disasters, thus ensuring safe operations and mitigating risks on high-speed railway routes [51]. The above review of studies shows that there has to this point been a lack of in-depth and comprehensive analyses involving the quantification of railway accidents and their causal relationship with meteorological factors. There is a dearth of concrete data-based evidence to support a causal relationship between weather conditions and accidents, while some papers have not adequately addressed the limitations and uncertainties of predictive models, thus making them less likely to be applied in practice. These research gaps must be addressed through the inclusion of more detailed data analysis, rigorous statistical methodologies, and causal inference techniques to establish strong associations between weather variables and rail accidents, which will ultimately provide valuable insights into preventing and improving safety from weather-induced accidents in rail systems. As discussed earlier, global warming is expected to gradually increase incidents of severe weather, extreme weather, and anomalies. Notably, these phenomena are also expected to lead to increased losses associated with such weather events. Rail systems and related infrastructure are particularly vulnerable to environmental and geographic factors, as they are constantly exposed to external atmospheric conditions. Moreover, as detailed above, many studies have reported that weather conditions can cause serious and extensive damage and accidents to railroad-related facilities. Worsening weather conditions due to global warming are expected to eventually cause many accidents and losses to railways and railway-related facilities [52; 53].