Neural Network Approaches to Modeling of Natural. Emergencies. Prediction of Lena River Spring High Waters

Abstract

Floods play a significant role in terms of damage and safety during construction and operation of crucial objects such as a bridge over the Lena river and underwater crossings of trunk pipelines in the North and the Arctic.A rapid rise of spring high water on the Lena river is due to accelerated melting of snow in a basin and a meridional flow direction of the river.If flood control measures are not taken, then severe economic and social consequences are inevitable, especially in places with complex infrastructure. As, for example, heavily populated cities, the strategically important objects, the underwater crossings of the trunk pipelines, bridges and power lines.This paper presents results of a study of a possibility of use of neural network algorithms to predict danger of the flood from the spring high waters on a section of the Lena river based on statistical archival data obtained over 70 years and an assessment of effectiveness of the neural network approach. The artificial neural networks have proven their effectiveness in solving various prediction problems, especially when using the statistical data. The use of the neural network approach based on the prediction of a time series from previous values gives the good results. Modeling was carried out using methods of a multilayer perceptron (MLP) and radial basis network (RBF). Both selected methods showed sufficient adequacy of selected statistical models.