Empirical Models Using Time Series Analysis For Forest Fire

One of the most dangerous factors that might threaten ecosystems, biodiversity, and human communities is forest fires. As such, predicting forest fires is considered a crucial step in disaster management. This paper presents the application of machine learning and time series analysis techniques in the prediction of forest fire outbreaks based on historical data. It uses a database of reports of fire incidence and utilizes the statistical method to discover patterns and trends underlying. Once that data is preprocessed by testing stationarity using an Augmented Dickey Fuller test, the ARIMA model applies for forecasting the future outbreak of fires. The error metrics MSE have been used to check the performance of the model and the outcome shows that ARIMA has been capable enough to catch the trends and seasonality of fire occurrence. This paper is showing some capabilities of data-driven approaches to forest fire prediction by drawing attention towards the important aspect of early warning for the proactive management of disasters. Future work includes incorporating further variables, such as weather and vegetation data, into the model and exploring more sophisticated machine learning models for the improvement of prediction accuracy.