MARINE FIRE-DANGEROUS SITUATIONS FACTORS’ VALUES FORECASTING BASED ON ARTIFICIAL NEURAL NETWORK

Abstract

The article describes the procedure of marine fire-dangerous situations factors’ values forecasting based on artificial neural network. These factors are temperature, optical air density, aerosol concentration. Given procedure is flexible and can be expanded for other factors of fire-safety state of monitored object. Artificial neural network with architecture of three-layer perceptron is used for forecasting. The article gives a common scheme for realization of fire-dangerous situations factors’ values forecasting, substantiates the choice of used artificial neural network’s architecture, gives perceptron learning algorithm. As a result of given procedure execution factors’ values forecasting is implemented for prevention of fire-dangerous situation and the adoption of early actions. In case of integration of the developed procedure inside ship information management systems’ algorithmic support is capable of dramatically raise effectiveness of decisions made while providing fire safety on ships.