Deep Learning-Based Prediction of Fire Occurrence with Hydroclimatic Condition and Drought Phase over South Korea

Jang Hyun Sung, Young Ryu, Kee‐Won Seong

KSCE Journal of Civil Engineering · 08 February 2022

In one sentence. A deep belief network driven by relative humidity, wind speed and drought index estimates monthly forest fire occurrence in South Korea with R² of 0.84.

Abstract

This study evaluates the effects of climatic conditions and drought phase on occurrence frequency (OF) of forest fire in South Korea, and suggested a deep learning-based estimator for the occurrence frequency of fire. There was a high correlation between frequency and relative humidity (RH, R = −0.663) and wind speed (WS, R = 0.532). Using these correlations, we proposed a deep learning model that can estimate the OF. Among the three deep learning models (RH-WS-AMOF, RH-AMOF, and WS-AMOF) by combining RH and WS with average monthly OF (AMOF) during 1997–2019, the RH-WS-AMOF model showed the best performance. R 2 and NSE were 0.838 and 0.828, respectively. The higher temperatures and drought lead to increase the potential for forest fire. Standardized precipitation evapotranspiration index was introduced for exploring the link between meteorological drought and forest fire. We confirmed that SPEI can improve the performance of the DBN based on OF estimator in spring. The framework of this study can provide a predictive model for forest fire OF combined with a weather forecast model.

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