Early Detection and Prediction of Some Threats in Complex Distributed Systems Based on Data Mining

Abstract

A method for predicting threats in complex distributed systems is proposed, based on the intelligent analysis of large data arrays on the results of monitoring changes in water level in water bodies and air temperature at the measurement point, which makes it possible to increase the efficiency of planning and implementing measures to fend off such and similar threats. The method is based on general approaches and mathematical models previously used by the authors to develop adaptive algorithms for controlling gas turbine engines, which is especially relevant in the context of the increasingly widespread introduction of automatic means for monitoring the state of complex distributed systems and the exponential growth in the number of data used to support decision-making. The choice of the future value of the water level at the measurement point is carried out based on the results of processing the data accumulated for all previous flood periods on the compliance of the water level and its changes per day with the values of air temperature and its changes for the same day. The results of an experimental assessment of the accuracy of predicting the water level in the water bodies of the Republic of Bashkortostan in the flood period of 2021 are presented, which confirm the applicability of the proposed forecasting method to support decision-making to fend off threats in complex distributed systems from a sharp rise in water.