# Heatwave Damage Prediction Using Random Forest Model in Korea
## 1 Introduction  
According to the National Center for Environmental Information of the National Oceanic and Atmospheric Administration, the average annual global temperature has reached an all-time high over the past five years (0.75-0.95 \({}^{\circ}\)C rise from the average annual temperature in the 20 th century) and is continuing to gradually increase. Global warming has considerably changed the climate in recent decades, increasing the probability and intensity of meteorological and climatic disasters [1; 2]. The duration and intensity of heatwaves are expected to increase with an increase in the average annual temperature, and deaths from heatwaves are expected to double [3]. The record heatwave in the United Kingdom in 2003, which killed 70,000 people, is expected to become normal summer weather by 2040 [4]. Because heatwaves cause human and physical disasters every year, it is important to minimize disaster damage by establishing timely and preemptive disaster responses. A disaster response is a continuous decision making process conducted on the basis of a variety of information and past experiences that are continuously gathered from a range of locations. Further, disaster response is conducted from the moment a disaster is perceived to have occurred until the time when it ends. In the past, data collection techniques were less effective and provided limited information for use incontextual judgment and decision making. Consequently, owing to the lack of information available for contextual judgment and decision making, disaster responses were highly dependent on the subjective experiences of decision makers. Furthermore, although data collection technology has developed rapidly with an increase in the information available for decision making, the capability of humans to process and use this information in disaster response is limited, especially in cases that require swift decision making. The importance of utilizing big data and artificial intelligence (AI)-based analysis for the rapid processing of various types of data has been recognized. Big data refers to large and diverse forms of data that cannot be processed by traditional database systems. Further, big datasets can include signals, images, and documents whose sizes increase exponentially; such data are abundant, owing to the development of sensing and social media-oriented communication technologies within the present Internet of Things environment [5; 6]. Big data systems not only utilize a variety of data quickly but are also expected to play a crucial role in analyzing meaningful information. However, early systems only focused on data collection and storage [7]. To produce meaningful results from big data, AI technology as well as simple statistical and visualization functions must be employed for analysis and prediction. Heatwave definitions vary among different countries [8]; however, heatwaves are generally defined on the basis of the normal weather and temperatures corresponding to the seasons of a region, and they are said to occur when there is a large deviation from the normal climate pattern in a given region. These extreme weather conditions occur locally and extensively, which limits rapid disaster response. In particular, because such extreme weather conditions occur extensively throughout a region, response procedures, such as preparing resources immediately in the event of a disaster, are limited. This indicates the need to develop early warning systems to guide disaster responses. Previous studies have focused on mortality as an endpoint for the analysis of damage caused by heatwaves [9; 10; 11], and only few studies have focused on morbidity as an indicator [12]. In addition, most studies have adopted only weather-related parameters as predictor variables of mortality. However, even under the same weather conditions, the damage pattern can vary, and it depends on other variables, such as the vulnerable population.