Mert NakıpNur KelesogluCüneyt GüzelişGuzelis, CueneytKelesoglu, NurNakip, Mert2025-10-062024156849461568-49461872-968110.1016/j.asoc.2024.1120232-s2.0-85199552821https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199552821&doi=10.1016%2Fj.asoc.2024.112023&partnerID=40&md5=54c874d3464f15dcd09e2f334b3c36b4https://gcris.yasar.edu.tr/handle/123456789/8155https://doi.org/10.1016/j.asoc.2024.112023We 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. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessFire Detection, Flattening-samples Based Augmented Regularization, Forest Fire, Hybrid Neural Networks, Risk Assessment, Benchmarking, Deforestation, Fire Detectors, Fire Hazards, Long Short-term Memory, Multilayer Neural Networks, Network Architecture, Risk Assessment, Fire Detection, Fire Risks, Flattening-sample Based Augmented Regularization, Forest Fires, Hybrid Neural Networks, Hybrid Support, Multi Sensor, Regularisation, Risks Assessments, Support Vector Regressions, FiresBenchmarking, Deforestation, Fire detectors, Fire hazards, Long short-term memory, Multilayer neural networks, Network architecture, Risk assessment, Fire detection, Fire risks, Flattening-sample based augmented regularization, Forest fires, Hybrid neural networks, Hybrid support, Multi sensor, Regularisation, Risks assessments, Support vector regressions, FiresForest FireRisk AssessmentHybrid Neural NetworksFire DetectionFlattening-Samples Based Augmented RegularizationArticle