A system based on an artificial neural network of the second generation for decision support in especially significant situations.

Currently, specialized system models implemented on the basis of decision support in exceptional (emergency) situations (states) using machine learning, artificial intelligence (including using neural networks) to reproduce, predict and prevent (or minimize the risk of consequences) in exceptional situations are useful and are becoming increasingly popular. Floods also fall under such exceptional situations and states, on the basis of which the task of early forecasting of an exceptional situation arises, using the example of rising water levels at stationary hydrological posts in order to prevent (or minimize the risk) the transition of the territory management system under consideration to an exceptional state (emergency situation). To solve this problem, a decision support system is proposed for early prediction of water rise levels, based on a neural network (intelligent) analysis of retrospective data (code of a stationary hydrological post / automatic station, date, water level at a stationary hydrological post / automatic station, atmospheric pressure, wind speed, snow cover thickness, amount of precipitation, time and air temperature) in order to calculate the values of water levels for 5 days in advance. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network (ANN) learning rate increase factor. A mathematical model of the subject area is constructed in the form of a multidimensional space and an integrated multidimensional data model. A single integrated model describes the subject area quite fully and can be used as a single source of information, which provides system information support, defined as a set of distributed databases. All this makes it possible to improve the accuracy of forecast values of water levels at stationary hydrological posts and automatic stations. The main difficulty in early forecasting is a sharp rise in water levels over a short period of time (usually 1 day) for various reasons. Our neural network model takes this into account and allows us to give more accurate results. The results of the analysis of the effectiveness of the method showed that the proposed decision support system is more accurate: the average error does not exceed 55.68 cm for the entire flood period, the average error between the real and calculated values in the framework of forecasting for 5 days does not exceed 2.10 % compared to existing common methods/systems (8.36 %). This will give the necessary time to special services to carry out anti–flood measures to prepare for the protection of technical facilities of enterprises.