Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques
Journal:
arXiv
Published Date:
Mar 13, 2025
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.
