# Heatwave Damage Prediction Using Random Forest Model in Korea
## 4 Discussion and Conclusions  
Heatwave damage prediction has been investigated in the United States, Europe, and Asia [48; 49; 50; 9]. However, existing predictions are limited to practical notification systems owing to unrepresentative data and insufficient data accuracy [51]. According to previous research, this problem is due to the use of heatwave mortality alone as the endpoint of damage. Because the mortality rate of heatwaves is exceedingly small compared to that of the general population, it is more effective to predict risk by morbidity, which is relatively higher in proportion than heatwave mortality. \"Temperatures exceeding 33 \({}^{\circ}\)C\" is the only available criterion for identifying the danger of heatwaves in South Korea, which allows the government to raise risk awareness by alerting the public. However, the damage to the population (deaths and sickness) caused by heatwaves varies even at the same temperature. Therefore, using only temperature data cannot determine the level of damage to peoples' health. On the basis of epidemiological investigations performed in previous research, we selected relevant variables and evaluated them by the Boruta algorithm. Then, a random forest-based heatwave damage prediction method was proposed, and its performance was compared with other traditional models. Previous studies considering demographic information have mainly used data with static characteristics, such as monthly statistical information. More accurate predictions were achieved by matching the exposure to heatwaves in a specific area to the population in that area  
Figure 6: (**a**) Visualization of predicted number of heatwave-related patients in the second week of June 2020 in South Korea. (**b**) Predicted and observed data for number of heatwave-related patients in Seoul over a month. using dynamic population data updated more frequently, allowing this variable to contribute more to the prediction. In the evaluation of the importance of variables, the average temperature variable and the number of occupational groups that are considered to be vulnerable to heat waves were highly evaluated (the average temperature for a week is the sum of the week, that is, the accumulated temperature). This result supports the importance of predicting the cumulative temperature in advance and responding in advance in order to minimize heat damage. In addition, it provides grounds that preemptive responses from the government, such as operation of sprinkler trucks and installation of shade curtains, should be made in areas with many vulnerable occupations. The learning process performed to build the machine learning model used independent variables based on previously recorded data. However, it is difficult to apply big data to a real-time environment owing to limitations such as irregularity in the frequency of data. Furthermore, when the time series of the dependent and independent variables are configured identically in training, the conditions for predictions are not effectively established in practical systems. Therefore, a method that employs predicted variable values was proposed. Although the accuracy of predicting future patients by applying predicted data is lower than the test accuracy during validation, the \(R^{2}\) value of 0.70 supports the fact that this model provides reasonable information. Governments can use the methods developed in this study to provide disaster response decision makers with a reasonable basis for prioritizing an administrative area to provide a preemptive response and disaster support. Nonetheless, this study has several limitations. First, the temporal resolution of the predicted values is relatively coarse-grained; thus, it is impossible to provide daily predictions for heatwaves, which are expected to occur every day. Most studies on heatwaves in South Korea have provided weekly information [11; 49]. As a result, it was inferred that the resolution of target values (heat patients) is tailored to the minimum information time unit of data source.