Seasonal Patterns and Forecasting of Burned Areas during Thailand’s Fire Season: A Comparative Time Series Approach 10.32526/ennrj/25/20260112
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Abstract
Forest fires are a major environmental concern that affect ecosystems and human activities, particularly during Thailand’s main fire season. Understanding seasonal burned area dynamics is important for supporting fire monitoring and management. This study analyzed monthly burned area data from January to April for the period 1999-2024 to examine seasonal patterns and compare the forecasting performance of three time series methods: moving average, single exponential smoothing, and a regression based seasonal model. Forecasting performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed seasonal patterns and interannual variability in burned areas. Among the evaluated methods, the moving average model with a four-month window (MA(4)) achieved the lowest MAE and RMSE values. The regression-based seasonal model produced comparable forecasting errors, whereas exponential smoothing with α=0.3 showed higher error values. Residual analysis indicated no clear systematic pattern or persistent temporal trend, suggesting that the main temporal variation during the fire season was adequately represented by the forecasting models. These findings suggest that simple smoothing based forecasting methods may be suitable for short term forecasting of seasonal burned area data while providing a useful baseline for future studies.
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