Document Type : Research Paper


sirnak university


Fire, a natural calamity, poses a severe threat to human existence and is typically combated utilizing traditional measures. However, due to the limitations and potential environmental damage associated with these conventional strategies, there is a compelling need to devise innovative firefighting techniques. Among these emergent strategies, fire suppression systems utilizing sound wave technologies present a promising alternative.

In the present investigation, a comprehensive compilation of data harvested from a series of experimental trials focused on sound wave-based extinguishment was employed. Utilizing these data, a fire suppression model predicated on sound wave technologies was architectured, integrating five distinct fuzzy logic methodologies. These include Fuzzy Rough Set (FRS), Fuzzy K-nearest neighbors (FNN), Fuzzy ownership K-nearest neighbors (FONN), Fuzzy-rough K-nearest neighbors (FRNN), and Vaguely quantified K-nearest neighbors (VQNN).

The ultimate goal of these models is to accurately discern between the extinguished and non-extinguished state of the flame. This classification is based on a variety of input parameters intrinsic to the model, such as fuel type, flame size, decibel level, frequency, airflow, and distance parameters.

Evaluation of the models' classification efficacy was carried out through a combination of various statistical methods including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), kappa statistic (KP), and Mean Squared Error (MSE).

The analysis yielded encouraging results, with FRS, FNN, FONN, FRNN, and VQNN models demonstrating classification accuracies of 93.12%, 96.66%, 95.56%, 96.35%, and 96.89% respectively. Hence, it was concluded that the proposed model exhibits high accuracy in classifying firefighting data, affirming its applicability.


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