Time-Series Model for Climatological Forest Fire Prediction over Borneo
Abstract
Areas covered by tropical forests, such as Borneo, are vulnerable to fires. Previous studies have shown that climate data is one of the critical factors affecting forest fire. This study aims to predict the forest fire over Borneo by considering the temporal aspects of the climate data. A time seriesbased model, Long Short-Term Memory (LSTM), is used. Three LSTM models are applied: Basic LSTM, Bidirectional LSTM, and Stacked LSTM. Three different experiments from January 1998 to December 2015 are conducted by examining climate data, Oceanic Nino Index (ONI), and Indian Ocean Dipole (IOD) index. The proposed model is evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and correlation number. As a result, all models can capture the spatial and temporal pattern of the forest fires for all three experiments, in which the best prediction occurs in September with a spatial correlation of more than 0.75. Based on the evaluation metrics, Stacked LSTM in Experiment 1 is slightly superior, with the highest annual pattern correlation (0.89) and lowest error (MAE= 0.71 and RMSE=1.32). This finding reveals that an additional ONI and IOD index as the prediction features would not improve the model performance generally, but it specifically improves the extreme event value.
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