Optimizing fire alarm systems with machine learning

A study published in arXiv (2025) titled “Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning” has shown how hybrid AI models can significantly reduce fire detection errors. One of the main challenges in fire alarm systems is false alarms caused by steam, dust, cooking fumes and temperature changes. Using a combination of K-Nearest Neighbors (KNN) and XGBoost algorithms, the researchers managed to build a model that analyzes data from various sensors (smoke, gas, temperature and image) and makes the final decision intelligently. The result of this research was a reduction of more than 75% of false alarms compared to conventional systems. In the next phase, the system can adapt to new environmental conditions with continuous learning; for example, it can determine how much steam in the kitchen is normal and when it indicates a real danger. 🔹 Application for you: Companies active in the field of fire alarm systems consulting can use this approach in designing the next generation of control panels — especially for office buildings and hospitals where reducing false alarms is critical.