Papers › Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
Mert Nakıp, Cüneyt Güzeliş, Osman Yildiz
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fire Detection | NIST Report of Test FR 4016 | rTPNN | F1-Score | 0.93 | #1 of 3 | Archive leaderboard | report |
| Fire Detection | NIST Report of Test FR 4016 | rTPNN | MCC | 0.9 | #1 of 3 | Archive leaderboard | report |
| Fire Detection | NIST Report of Test FR 4016 | LSTM | F1-Score | 0.86 | #2 of 3 | Archive leaderboard | report |
| Fire Detection | NIST Report of Test FR 4016 | LSTM | MCC | 0.82 | #2 of 3 | Archive leaderboard | report |
| Fire Detection | NIST Report of Test FR 4016 | MLP | F1-Score | 0.73 | #3 of 3 | Archive leaderboard | report |
| Fire Detection | NIST Report of Test FR 4016 | MLP | MCC | 0.66 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: rTPNN
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