EARLY FIRE HAZARD PREDICTION FRAMEWORK IN SMART CITIES USING DEEP LEARNING WITH ANTLION OPTIMIZATION ALGORITHM

This study aims to refine the early fire risk prediction model for evaluating the accurate locations of fire using sensor data. Internet of Things (IoT) is integral to smart cities. IoT applications in smart cities include crime predictions, traffic optimization and monitoring of health and environmental conditions. This article reports a study on using Recurrent Neural Network (RNN) with Ant Lion optimization (ALO) framework to enhance and refine the prediction of fire hazards. IoT sensors in smart cities monitor the environmental conditions such as drought, temperature, smoke, flame, relative humidity, fuel moisture and duff moisture. This sensed data is stored in the firebase cloud storage and analyzed in the MATLAB tool. The ensuing enhancement of the proposed model is validated by comparison with conventional prediction models. Our results indicate gains in accuracy and reduction in error rates in fire hazard predictions.