Abstract
Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015–2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable.
Highlights
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 ◦ C rise from the average annual temperature in the 20th century) and is continuing to gradually increase
After separating the dataset comprising the selected variables into training and test datasets, we evaluated the model trained using the training dataset by comparing it with other traditional regression models
In South Korea, big data regarding the floating population are estimated on the basis of mobile big data collected hourly and monthly by SK Telecom’s nationwide mobile communication base stations, and the estimated data are obtained from the Statistical Data Center
Summary
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 ◦ C rise from the average annual temperature in the 20th 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]. 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. Disaster response is conducted from the moment a disaster is perceived to have occurred until the time when it ends
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