A Comparative Analysis of Machine Learning Approaches for Sound Wave Flame Extinction System Towards Environmental Friendly Fire Suppression
Robert G. de Luna, Zenesca Ann P. Baylon, Coreen Anne D. Garcia, Jose Rogelio G. Huevos, John Lester S. Ilagan, Maria Jamaica T. Rocha
Abstract
The devastation caused by fires is a significant threat to human life. There are traditional fire extinguishing methods but can have negative impacts on the environment. This study utilized data from a system that uses sound waves to extinguish fires without requiring water and chemicals. This paper created machine learning models that can predict whether a fire can be extinguished by the sound waves given the features like the size, fuel, distance, decibel, airflow, and frequency. The researchers used Python programming to create different machine learning models and determined the most accurate model using the classification accuracy and F1 score as performance metrics. The XGBoost model was identified as the most effective in classifying the sound wave flame extinction with accuracy scores of 98.31 % and 98.62% for the model with default and optimized parameters, respectively.