Heatwaves are natural disasters that cause most human casualties. Owing to the influence of climate change, the frequency and intensity of heatwaves have increased, causing considerable damage in Korea. Even at the same temperature, the heatwave can exhibit different damage patterns depending on environmental and socioeconomic factors. Therefore, it is essential to analyze the influencing factors of heatwaves for effective preparation and response. In this study, partial least squares structural equation modeling was used to quantitatively analyze the direct and indirect effects of heat-related variables on heat-related illnesses. Both the reflective measurement and structural models met the evaluation criteria. In addition, it was confirmed that the economy and urbanization acted as influencing factors of -0.385 and -0.623 on the vulnerable group, respectively, and the vulnerable group had a value of 0.534 for heat-related illness. This study helped comprehensively understand the relationship between heatwave-influencing factors and heat-related illnesses. This study method can be used to investigate the causal relationships among various factors in terms of preparing for and responding to heatwave damage.

Recently, the incidence of heat waves has increased due to climate change, and the resultant mortalities and socio-economic damage are also increasing in Korea. Hence, emphasis has been placed on research examining heatwaves and their effects. Predicting the probability of heatwave in advance is very important from the perspective of disaster risk management; however, related studies have been insufficient so far. Therefore, in this study, the probability of future heatwave onset was predicted using daily scaled past weather data for Seoul Metropolitan Government. For the analysis, models based on recurrent neural networks (RNN, LSTM, GRU) were used, which are suitable for analyzing time-series data. Upon evaluating the performance of the GRU model, which was selected as the optimized model, no overfitting problem was observed. The prediction accuracy of the model was high as it demonstrated a reproduction of 78% and 86% of actual heatwave days during the validation and test process, respectively. Therefore, this model can be used by each local government to coordinate an efficient response to heat waves.

The current heatwave crisis alert system considers the daily maximum temperature alone; therefore, the present study proposed a new method for estimating the daily heatwave crisis alert level. Specifically, the heatwave risk index was calculated by combining regional vulnerability, as estimated using principal component analysis, and normalized daily maximum temperature. The crisis alert level was classified according to the risk index value using the natural breaks method. The correlation coefficient between the heatwave risk index and heat-related morbidity rate was approximately 0.4. In addition, analysis of variance confirmed significant differences in the heat-related morbidity rate among different crisis alert levels. The current method is limited in that it does not take into account the various regional environmental characteristics, which may results in an ambiguous alert standard; however, this shortcoming may be resolved using the proposed heatwave risk index. Therefore, the results of the present study may be valuable for risk assessment and management in Korea.