Theoretical identification methods for complex industrial control objects give very cumbersome and complex mathematical relations, the use of which for practical purposes is not constructive. In this regard, methods for obtaining mathematical models based on experimental data have now become the main focus of identification theory. In this paper is described the method of identification of industrial control objects developed according to their acceleration characteristics. The structure of the object under study is determined by the type of amplitude-phase frequency response and dynamic parameters are determined by experimental data. The high adequacy of the method is confirmed by similar studies on known (reference) models. The scientific novelty of the work consists in development of a new method for identifying complex industrial control objects by their acceleration characteristics.

The densely populated central part of the Ile Alatau mountains is one of the most mudflow-prone areas of Kazakhstan. Implementation of protection measures, early warning systems, and risk management plans is crucial to protect livelihoods and infrastructure from damage caused by mudflows. Increasing harm and damage from mudflows in recent decades—due to more frequent events, as well as increased economic development of the area—has made the establishment and implementation of a mudflow risk management system a priority. The effectiveness of such a system largely depends on the scientific validity of the management plan and hence is determined by the level of knowledge of the physical processes triggering such events. This knowledge is based on information that must be collected, analyzed, and systematized. However, such data are not easy to access; they are scattered over different archives and research institutes or simply missing. In recent years, scientific monographs, articles, and reports have been published that attempt to collect and systematize data on mudflow phenomena in general. These efforts provide a basis for further work but are often not readily available for use. This article presents the updateable, interactive, intelligent information system “Mudflow phenomena of the central part of the Ile Alatau” that links cartographic information with data on mudflow formation centers. This system concentrates and collates existing knowledge, making it accessible to stakeholders and decision-makers who can turn this knowledge into suitable applications for adaptive and sustainable risk management.