Forest fire detection using spiking neural networks

Abstract

Forest fires is one of the main causes of environmental degradation and its detection and forecasting is challenging. A novel method of forest fire detection based on spiking neural networks is proposed in this paper. Data obtained from controlled experiments are used as input training samples and a detection model is established by considering the factors of temperature, humidity, carbon monoxide concentration, wind speed and wind direction. Experimental results show that the spiking neural network can achieve a detection accuracy of ∼91%, and therefore provides a better power/accuracy trade-off against existing approaches.