An Investigation into the Risk Factors of Forest Fires and the Efficacy of Machine Learning for Detection

Forest fires are a major environmental hazard that can have significant impacts on human lives. Early detection and swift action are crucial for controlling such situations and minimizing damage. However, the automatic tools based on local sensors in meteorological stations are often insufficient for detecting fires immediately. Machine learning offers a promising solution to forecast forest fires and reduce their rapid spread. In recent state-of-the-art solutions, only one or two techniques have been utilized for prediction. In this research, we investigate several methods for forest fire area prediction, including Long Short Term Memory (LSTM), Auto Regressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Our aim is to identify the most effective and optimal method for predicting forest fires. After comparing our results with other artificial intelligence and machine learning techniques applied to the same dataset, we found that the LSTM approach outperforms the ARIMA and SVR predictors by more than 92%. Our findings also indicate that the LSTM algorithm has a lower estimation error when compared to other predictors, thus providing more accurate forecasts.