Bushfire Risk Detection Using Internet of Things: An Application Scenario

With rising temperatures and events contributing to climate change, the world is facing extreme weather patterns. Recently, Australia was hit hard by bushfires, the most devastating fires ever faced by the country. The economic damage reported was nearly one billion Australian dollars and an estimated three billion native animals were killed or adversely affected. Given the extent and intensity of this damage, researchers are seeking effective solutions to enable the prediction of fire before it starts to increase the time available for firefighters to protect lives and assets and prepare to mitigate the fires. This motivated us to investigate an approach to address this critical problem. In this article, we propose a machine learning (ML)-based approach that detects anomalies in spatiotemporal measurements of environmental parameters (e.g., temperature, relative humidity, etc.). In the proposed approach, an ML-based model learns the normal spatiotemporal behavior of the environmental data (collected over a period of one year). This is carried out during a one-time training phase. Then, during the detection phase, any spatiotemporal pattern in the real-time data (received from the field sensors) that is different than the normal pattern will be identified by the model as anomaly which indicates a possible bushfire situation. Following this, we propose a supplementary classification model based on Moran’s I index to ensure that the detected anomalies are not due to either a sensor failure or a security attack (which are common in Internet of Things). We developed three different ML models for performance evaluation and comparison and used the Forest Fire data set to train them. The results of our experiments confirm the effectiveness of the proposed approach in the early detection of fire symptoms.