Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization

Abstract

We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination facilitating the integration of l1 regularization into the SVR method thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires benchmarking against 15 machine learning models including state-of-the-art techniques such as Recurrent Trend Predictive Neural Network (rTPNN) Long-Short Term Memory (LSTM) Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire enabling early preventive measures. Notably it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly it exhibits computation times significantly lower - by 1 to 2 orders of magnitude - than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR facilitated by flattening-samples based augmented regularization allows for high detection performance even with smaller training sets.