Introduction. At the present stage, the task of high-quality and effective processing of a large amount of information that is collected and accumulated in databases, reference books, documents and other sources of information in the field of fire prevention and liquidation and emergency situations, fire safety is becoming more and more urgent. One of the most popular methods of information analysis is cluster analysis, which allows dividing the set of objects and features under study into homogeneous groups (clusters). As a result of cluster analysis, it is possible to identify patterns of changes in the parameters (values) under study, classify them and transform information into knowledge. Goals and objectives. The purpose of the review is to analyze Russian and foreign scientific publications to identify the main directions and methods of using cluster analysis, special software, development tools and programming languages. Methods. During the review of scientific publications, information was collected from open sources, a list of publications was formed taking into account the accepted selection criteria and search keywords. As a result, the classification of the directions of application of cluster analysis in the field of fire prevention and liquidation and emergency situations, fire safety is carried out. Results and their discussion. The analysis of the reviewed publications shows that cluster analysis methods using modern computer technologies are used in a wide range of research in the field of fire prevention and emergency response. In general, it is possible to classify six main areas of application of cluster analysis in the subject area under consideration. When conducting cluster analysis, in most cases, universal statistical software tools are used, as well as the authors' own developments and high-level programming languages with numerous libraries for multidimensional data analysis. In methodological terms, conducting research on cluster analysis requires a systematic description of all stages of work. This is especially true for the issues of standardization (normalization) of data and evaluation of the quality of clustering. Conclusion. The paper reviews 15 scientific Russian and foreign publications on the application of cluster analysis in the field of fire prevention and emergency response. As a result of the analysis, the main directions of application of cluster analysis in the subject area under consideration, clustering methods, applied tools and programming languages are determined. Proposals are formulated for the further development of the application of cluster analysis, taking into account the need for a more detailed description of the stages of its implementation. Keywords: cluster analysis, fire, emergency, multidimensional data analysis, statistics.
Introduction. Timely response to emergency situations is an urgent task in the framework of improving management in the structure of EMERCOM of Russian. The high need for the rapid creation and development of digital products, the management of digital services require a rethinking of approaches to the organizational and functional structures of government bodies. The rapid development of artificial intelligence models and algorithms inevitably leads to digital transformation processes, including at the stage of emergency response. To identify problems at the stage of responding to emergencies, a survey questionnaire was drawn up for specialists and employees of Crisis Management Centers of EMERCOM of Russia. The system of Crisis Management Centers of EMERCOM of Russia provides a solution to many complexly structured control tasks. Targets and goals. The aim of the work is to determine the priority of emergency criteria for their subsequent classification and search in the knowledge base. In order to achieve this goal, it is necessary to conduct a survey of specialists of Crisis Management Centers of EMERCOM of Russia on the widest range of issues in the field of emergency response. Methods. In the course of the work, statistical and dispersion-correlation analysis of the obtained data was carried out. Expert opinions were ranked, after which their consistency was determined. Results and its discussion. Conducted a survey of employees of Crisis Management Centers of EMERCOM of Russia (430 specialists) in 15 constituent entities of the Russian Federation, survey results were analyzed in order to obtain data to determine the number of response forces and means. A priority sequence of criteria has been obtained for three types of emergency situations: transport accidents, explosions (including those with subsequent burning) and (or) destruction (collapse) in buildings and structures, accidents in life support systems. This sequence is necessary to classify and search for emergencies in the knowledge base in order to determine the required number of forces and means to respond. Conclusions. The application of the results of the work will allow carrying out procedures for classifying emergencies, ranking their criteria, as well as determining the required number of forces and means for response. Keywords: survey, emergency, crisis management center, dispersion-correlation analysis, classification, ranking.
Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.
Introduction. The simulation of fire development and suppression processes must take account of a large number of random factors concerning the fire environment and the resources, available for its putting out. An important feature of the fire development is its step-by-step nature, whereby one phase (stage) is naturally replaced by another as a result of physical combustion processes and decisions made amid certain states of fire. In the practice of modeling multiphase (multistage) processes, such models as decision trees, multistep positional games, random processes, including discrete Markov chains, and others are widely used. Each of these models has its own structure and parameters. The choice of the model structure for a particular application represents a heuristic step. In almost every case, parameters of models are set on the basis of logical inferences, physics, ongoing processes and available statistical data about the simulated phenomenon. This approach is usually referred to as normative. Its alternative is an adaptive approach, whereby model parameters are evaluated using historical data. This approach allows to make models that are sufficiently similar to real objects and capable of adapting to the nonstationary features of the environment and the changeability of the decision maker’s preferences.The relevance of the study lies in the development of a machine learning technology for the Markov models of the fire development process, which allow predicting the completion time of individual phases and the whole fire. The Markov model can also serve as the basis for determining the optimal fire rank.Goals and objectives. The aim of the work is to create and test the technology for designing models that allow to make projections of the fire completion time. The tasks of the model machine learning and its use as a tool for making projections and determining the rank of fire are set in line with this goal.Methods. The authors used methods of the theory of random processes, mathematical statistics, simulation modeling, technical and economic evaluations. The research is based on materials extracted from domestic and foreign publications.Results and discussion. The proposed method, designated for the machine learning of the Markov chains using statistical data on the response time of firefighting and rescue units, coupled with the use of trained models, technical and economic evaluations for assigning optimal fire ranks allow to apply algorithms built on their basis as part of fire safety decision support systems.Conclusions. The presented solutions to the problem of designing adequate models designated for projecting fire development phases and assigning fire ranks serve as the basis for effective decision support systems in terms of the short-term fire safety management.
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