Modernizing Wildfire Management Through Deep Learning and IoT in Fire Ecology

Abstract

The increasing frequency and severity of wildfires present critical challenges to ecosystems, human safety, and property and underline the inefficiency of traditional methods of fire detection and management. This chapter will present how integration between DL and IoT could give way to a revolution in fire ecology by providing innovative tools for real-time fire prediction, detection, monitoring, and response. DL, in particular, through Convolutional and Recurrent Neural Networks, looks into terabytes of data ranging from historical fire data to weather patterns and topography to predict and assess wildfire risks. IoT aids this with real-time data that emanates from a network of sensors, drones, and cameras spread across susceptible areas. This synergy therefore offers DL and IoT more accurate, timely, and proactive fire management. Future technologies will focus on 5G, blockchain, and advanced robotics for more resilient fire management strategies.