Forest Fire Prediction using LSTM

Forest fires devastate natural life and demolish lands that provide a livelihood. Accurate prediction of fire occurrence is vital in order to reduce its effects. Creating predictive models can help authorities estimate the long term effects of climate change on the forest distribution in the region. The economic and ecological impact of forest fires are significant and prediction can prevent the long term damage on the sensitive ecosystem of forests. This paper presents an investigation of wildfire prediction strategies dependent on computerized reasoning. With the help of previous weather conditions prediction about potential instances of wildfires is made. Temporal data of wildfires in India has been collected and is fed to an LSTM network, a time series forecasting Recurrent Neural Network (RNN) capable of predicting fire propagation more accurately. The model could predict the occurrence of forest fires with 94.77% accuracy. This model can be used by various organizations for conserving the rapidly diminishing forest covers in the country.