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http://dx.doi.org/10.5762/KAIS.2020.21.8.521

Data Mining based Forest Fires Prediction Models using Meteorological Data  

Kim, Sam-Keun (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Ahn, Jae-Geun (School of Computer Engineering & Applied Mathematics, Hankyong National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.8, 2020 , pp. 521-529 More about this Journal
Abstract
Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.
Keywords
Data Mining; Deep Neural Network Model; Support Vector Machine; Meteorological Data; Prediction;
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