A method for predicting the evolvement of an emergency zone based on artificial neural networks.

This article describes a method for evolvement predicting of an emergency zone on the example of a forest fire using neural networks and applying the Hurst exponent to increase the accuracy and reduce the computational complexity of the algorithm. The inputs in this method are a set of environment state maps and the fire characteristics. The lack of a sufficient number of maps and a large amount of available data has a negative effect on the forecast accuracy and the overall system performance. It proposed to divide the fire area into sectors and perform the forecast by sectors with an accuracy that depends on the sector behavior characteristics. The sector behavior characteristic in this paper presented by the persistence analysis of the sector vectors, and be replaced by any other indicator. Persistence indicates trend stability in the behavior of the time series and, accordingly, the forecast reliability. In the case of low forecast reliability, the sector is iteratively divided into segments, which are analyzed and characterized. In this case, forecasting carried out for each sector and segment, which generally increases overall forecast accuracy. The paper presents the key mathematical calculations and the results of experimental studies. [ABSTRACT FROM AUTHOR]