Accident Damage Prevention Technology

Abstract

The paper studies the methods of neural network modeling to prevent damage from accidents, compares different approaches to the analysis of time series, explores the mechanisms for estimating the accuracy of forecasting values, describes the models and uses them. The problem of choosing the optimal prevent damage from accidents model according to minimum forecast criterion error is stated and solved. To solve this problem, there was used the group of mathematical methods, including statistics and econometrics, such as: autoregression, moving average, exponential smoothing, and neural network modeling. The result of the study is an algorithm for estimation of possible accident damage. The model is based on minimizing the forecasting error and implements created algorithm.