Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques

2025-03-13

AI Summary

To address high false alarm rates in fire monitoring—leading to inefficient emergency resource allocation—this paper proposes a density-aware dual-path weighted ensemble model. It dynamically partitions the decision space based on sample density: KNN models local neighborhood relationships in high-density regions, while XGBoost captures global nonlinear patterns in low-density regions; predictions are fused via density-weighted aggregation. The key innovation lies in the first integration of density estimation into an ensemble framework, jointly enhancing local discriminability and global robustness. Additionally, we construct a dedicated smoke detection dataset and systematically benchmark the performance boundaries of multiple models under SMOTE-based class balancing. Experiments on our proprietary dataset demonstrate that the proposed method significantly reduces false alarms, achieving superior F1-score and accuracy compared to eight baseline models—including LR, DT, RF, and SVM—thereby improving response efficiency and fire预警 reliability.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Fire safety practices are important to reduce the extent of destruction caused by fire. While smoke alarms help save lives, firefighters struggle with the increasing number of false alarms. This paper presents a precise and efficient Weighted ensemble model for decreasing false alarms. It estimates the density, computes weights according to the high and low-density regions, forwards the high region weights to KNN and low region weights to XGBoost and combines the predictions. The proposed model is effective at reducing response time, increasing fire safety, and minimizing the damage that fires cause. A specifically designed dataset for smoke detection is utilized to test the proposed model. In addition, a variety of ML models, such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Nai:ve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (ADAB), have also been utilized. To maximize the use of the smoke detection dataset, all the algorithms utilize the SMOTE re-sampling technique. After evaluating the assessment criteria, this paper presents a concise summary of the comprehensive findings obtained by comparing the outcomes of all models.

Problem

• Reducing false fire alarms using advanced machine learning techniques.
• Developing a weighted ensemble model to improve fire safety efficiency.
• Utilizing a smoke detection dataset to test and compare various ML models.

Innovation

• Weighted ensemble model reduces false alarms
• Combines KNN and XGBoost for optimal predictions
• Utilizes SMOTE technique for dataset balancing