Scientific Journal of Astana IT University

14-21

Authors: V. Shevtsov, A. Ismailova, U. Aitimova, O. Khapilina

The pace at which scientific data is produced and disseminated has never been as high as it is currently. Modern sequencing technologies make it possible to obtain the genome of a specific organism in a few days, and the genome of a bacterial organism in less than a day, and therefore researchers from the field of life science are faced with a huge amount of data that needs to be analyzed. In this connection, various fields of science are converging with each other, giving rise to new disciplines. So, bioinformatics is one of these fields, it is a scientific discipline that has been actively developing over the past decades and uses IT tools and methods to solve problems related to the study of biological processes. In particular, a crucial role in the field of bioinformatics is played by the development of new algorithms, tools and the creation of new databases, as well as the integration of extremely large amounts of data. The rapid development of bioinformatics has made it possible to conduct modern biological research. Bioinformatics can help a biologist to extract valuable information from biological data by using tools to process them. Despite the fact that bioinformatics is a relatively new discipline, various web and computer tools already exist, most of which are freely available. This is a review article that provides an exhaustive overview of some of the tools for biological analysis available to a biologist, as well as describes the key role of information systems in this interdisciplinary field.

36-49

Authors: G. Azieva, A. Alimagambetova, U. Turusbekova

Kazakhstan is one of the few countries in the world rich in oil, deservedly called “black gold” because it is the most important source of energy. The relevance of the study of this paper is determined by the fact that the management of the oil industry affects not only the management process itself, but also the social aspects of the implementation of the development strategy of the state as a whole. It is necessary to identify aspects of management activity and define criteria by which it is possible to calculate the effectiveness of managerial decision-making in the analyzed industry. Agent models allow us to identify the main criteria for the effectiveness of managerial decision-making and optimize social and economic costs for their implementation within the framework of interdepartmental planning. The novelty of the research is determined by the fact that agent models are based not only on the associated parameters of the management process, but also affect the possibility of planning current activities for a long period. The article shows that the formation of agent models should affect both the aspect of the formation of matrices of complex managerial actions and calculations on the accounting of competencies in making managerial decisions. The practical significance of the study is determined by the fact that the development of complex models based on agent forms allows expanding the use of forms of control over the industry by the state and other stakeholders. The implementation of a matrix form of management is proposed, taking into account balanced industry indicators of management quality.

61-74

Authors: V. Yavorskiy, M. Yesmagambetova, S. Ussenov

The paper considers the features of the use of intelligent information technologies in the process of the emergency rescue service of the enterprise. To improve the efficiency of the rescue services, it is necessary to carry out strategic planning and management, which would be designed to prevent the occurrence of an emergency (emergency). Effective subsystems of emergency proactive planning should not only predict the occurrence of possible emergencies, but also provide for appropriate preventive measures, and emphasis should be placed on eliminating the underlying causes, not the emerging consequences. The change of modes in the event of an emergency can be effectively implemented through the deployment of a technologically secured situational center. Decisions in emergency situations are made in various operational situations, including crisis, and in extremely limited time. Nevertheless, they must be taken in a timely manner, be as reasonable as possible, and ensure the fullest and most effective use of available opportunities. Decision-making processes can be based on the personal experience of participants in the management process. As you carry out your professional activity, experience is formed, which subsequently allows you to perform some tasks much faster and more efficiently. To gain experience, it is proposed to use a knowledge management system based on ontology. In order to ensure maximum awareness of the management of the situation, it is proposed to use the expert system of the situation center. (ES SC) The ES of the situational center of the authorities can significantly improve the efficiency of management processes and provides information support for strategic and tactical management decision-making. The ES should implement tools for a comprehensive and operational assessment of the state of the management object and situational analysis of the identified problems.

85-94

Authors: A. Mukasheva, D. Yedilkhan, M. Aldiyar

The article is devoted to the description of the method of multidimensional database, which is an effective method of data storage, which allows analyzing data qualitatively, and most importantly in a short time. The article discusses the capabilities of multidimensional databases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes when analyzing large amounts of data. Provides an overview and features of a multidimensional database and discusses the steps you need to take with a multidimensional database to understand the structure and capabilities of an OLAP cube. To create a knowledge base, it describes the steps you can take to create and execute a multidimensional database that you can collect from various sources, save to a database, and then prepare a report using OLAP analysis. Various information system data processing technologies such as OLTP and OLAP were considered. The algorithm of the data storage process for analysis purposes was studied. A model of a multidimensional database in the form of a three-dimensional cube was presented. Examples of analysis and ways of obtaining information from the data cube were also given. The use of a multidimensional database in higher education institutions as a simple and effective method of data storage is considered. There are also illustrations of the structure of a higher educational institution to see the bulkiness of information, and what kind of information database operates in the educational institution.

14-23

Authors: Sharipa Temirgaziyeva, Batyrkhan Omarov

Road sign recognition is one of the most important steps drivers can take to avoid dangerous roads or accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of Road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors’ solutions were analyzed. In addition, in this work, the convolutional neural network (CNN) method with the help of deep learning was used to create a system for identifying and recognizing road signs. The proposed road sign recognition system can work in real time to recognize the image of road signs. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs. In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.

34-44

Authors: Arailym Tleubayeva, Assar Maidanov, Arina Kantayeva

Traditional street lighting systems receive data about daylight levels and adjust the lighting. However, in such conditions, energy consumption increases since the sensors of such systems receive data on only one indicator, which is daylight. Therefore, a suitable automated intelligent lighting system model is needed. Intelligent lighting systems can adjust the brightness of the light not only based on natural data but also based on the movement of vehicles and people. This paper describes the development, implementation, and testing of a smart lighting system model to increase energy efficiency and high reliability. This system is controlled by a microcontroller programmed to control the lighting and receive data from sensors for processing with good efficiency. Distributed sensors record environmental conditions such as daylight and traffic. Photoresistors change resistance in daylight to light up the streets at night. The HC-SR501 infrared motion sensor detects objects emitting infrared radiation (heat) in the controlled motion zone and sends a signal to the microcontroller. The intelligent lighting system uses LEDs, which consume less energy and achieve high efficiency. Calculations show that the efficiency of using these lamps is almost 70%, compared to what is used in conventional street lighting systems.

55-64

Authors: Dinara Kaibassova, Margulan Nurtay, Ardak Tau, Mira Kissina

Due to the popularity of the easiest way to obtain personal information among attackers, phishing detection is becoming a popular area for research aimed at countering the implementation of such attacks. Malicious website detection is essential to prevent the spread of malware and protect end users from victims. Unfortunately, malicious URL detection still needs to be better understood due to a lack of features and inaccurate classification. Possible sources were examined in order to investigate the subject. Based on the collected information from previous studies, this study is devoted to solving the problem of detecting phishing websites using Ensemble Learning. The aim of the work is to choose the most optimal algorithm for classifying phishing websites using gradient boosting algorithms. AdaBoost, CatBoost, and Gradient Boosting Classifier were chosen as Ensemble Learning algorithms and were used to improve the efficiency of classifiers. Practical studies of the parameters of each algorithm for finding the optimal classification model are given. Research and experiments were carried out on a dataset containing information extracted from the contents of a URL: main URL, domain, directory, and file. A thorough Exploratory Data Analysis (EDA) was carried out, as a result of which the main dependencies and patterns of determining phishing resources were identified using correlation analysis. ROC AUC Score was chosen as an evaluation metric for the algorithms. The best result for predicting phishing websites was demonstrated by the AdaBoost Classifier algorithm, with an average ROC AUC score of 99%. The results of the experiments were illustrated in the form of graphs and tables.

81-88

Authors: Ardabek Khompysh, Nursulu Kapalova, Kunbolat Algazy, Kairat Sakan

This article presents one of the main transformations of symmetric block ciphers used to protect confidential information, a new method for obtaining a non-linear S block, and an analysis of the results obtained. The S-box obtained by this method can be used as a nonlinear transformation in block cipher algorithms to protect confidential data transmitted over an open channel. In most well-known works in the field of analysis and synthesis of modern block symmetric ciphers, S-box is used as a mathematical apparatus for cryptographic Boolean functions. In this case, each S-box is represented by a set of composite Boolean functions whose properties characterize the efficiency of the nonlinear substitution node. Substitution nodes for modern symmetric primitives, including key unfolding functions, are usually implemented as replacement tables. Considering that in most modern block symmetric ciphers for introducing round keys, the encryption algorithm uses a linear operation (bitwise addition modulo 2), S-blocks are the only elements responsible for the cryptographic stability of block encryption algorithms. The required number of rounds of block symmetric ciphers is selected taking into account the results of the cryptographic analysis performed, provided that the properties of S-boxes are specified. As the main criteria and performance indicators, the balance and nonlinearity of composite Boolean functions are used; strict avalanche criterion (SAC), propagation criterion; algebraic degree; the value of the autocorrelation function. In this article, a study was made of the nonlinearity and strict avalanche criterion (SAC) of the S-box used in the block symmetric encryption algorithm. The results of the study were compared with the S-boxes of modern cryptographic algorithms and showed good results.

101-112

Authors: Alua Myrzakerimova, Katerina Kolesnikova, Mugulsum Nurmaganbetova

Today, the importance of information support for various medical technologies has increased significantly. The use of modern information technologies is becoming a critical factor in the development of most branches of knowledge and areas of practice, so the development and implementation of information systems is an urgent task. The clinical decision support system provides clinicians and stakeholders with individualized patient assessments and recommendations to assist in the clinical decision-making process. Knowledge-based information systems are widely used in medicine around the world. Modern technical capabilities make it possible to reach a qualitatively new level of presentation of the course of the disease, namely, based on appropriate mathematical models, to model the typical development of the pathological process in a particular disease, to speed up the process of diagnosing and receiving recommendations on treatment protocols. In Kazakhstan, there is no variety of decision support systems in medicine, especially in the process of diagnosing diseases. The purpose of this study is to develop the structure of an automated system for diagnosing diseases. The Unified Modeling Language (UML) is used as a design tool. The structure of the automated system is presented; the main components and key terms are considered. A mathematical model of diagnostics is shown (in the example of diseases of intestinal and pancreatic insufficiency) based on decision-making methods with fuzzy initial data. A diagram of the data flows of the system is presented. Thus, the paper proposes the structure of an automated system that will contribute to high-quality diagnostics due to an effective method of system organization and the use of fuzzy set theory methods.

122-138

Authors: Serik Omirbayev, Aidos Mukhatayev, Andrii Biloshchytskyi, Sapar Toxanov, Adil Faizullin, Svitlana Biloshchytska

The article considers the creation of structural model for the system of development of methodological competence of IT-discipline teachers based on continuing education with the description of the structure of microservices subsystems. To implement the proposed conceptual model the paper proposes a software architecture based on the use of classical service-oriented design pattern “Model-View Separation” (separation of model objects from the graphical interface objects with the user) and provides for interaction with the database, the composition of its main packages is detailed. Logical and physical service-oriented models of basic classes of objects of subject area and auxiliary objects (services) have been developed. The article also proposes a relational database structure based on the use of metadata tables, directories, and operational data in a relational database management system environment. Methods and mechanisms for data protection and replication are proposed and justified, considering the specifics of system operation in higher education institutions. The article aims to develop insurgents and mechanisms of creation of information technology improvement of the system of higher education and creation and operation of integrated flexible software tools to support the mixed system of organization of educational process, as well as increasing the effectiveness of the introduction of a mixed system for the organization of the educational process on the basis of the use of the latest technical means of training and documentation, ensuring free access of students to teaching and methodological resources, сontent and criteria for the assessment of knowledge in disciplines and modules, data recording, and partial compensation for the increased workload of teachers and staff through the automation of assessment, registration and assessment processes. A task-oriented model for managing technical problems in the implementation of the information system for the development of teachers’ methodological competence is also proposed, and a conceptual framework for managing these problems has been developed from the perspective of considering projects as a set of objects and subjects. Technical problems in projects are shown to be obstacles to moving in the right direction, created by random events, uncertainties, and changes. A mathematical model of the change in resistance value caused by technical problems has been developed and methods to counteract these problems have been proposed.

14-23

Authors: Gulnara Bektemyssova, Abdul Rahim Bin Ahmad, Sharafat Mirzakulova, Zhanar Ibraeva

The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic. This implies that the structure of network traffic will be heterogeneous, which requires deep analysis to find the internal features underlying the data. A common model for analyzing the processes of a multiservice network is a model based on time series. Numerous empirical data studies indicate that the packet intensity time series do not belong to the general aggregates of a normal distribution. The problem of predicting network traffic is still relevant due to managing information that flows into a heterogeneous network. In this work, the authors studied the time series for stationarity in order to select an appropriate forecasting model. A visual assessment of the series assumed non-stationarity. The Augmented Dickey-Fuller Test is applied, and the measured network traffic is predicted using the ARIMA (Auto-Regressive Integrated Moving Average) statistical method. Results were obtained using the Econometric Modeler Matlab (R2021b) application. The results of the autocorrelation function (ACF) and partial ACF are analyzed, with the help of which the ARIMA model is optimized. As a result of the study, a software algorithm for the ARIMA (0,2,1) model was developed.

36-47

Authors: D. Muratuly, N.F. Denissova, I.V. Krak

This article discusses the current problem of identifying violations during distance learning during the final certification. The coronavirus (COVID-19) pandemic has served as a stimulus for innovation in the field of education in all countries, including Kazakhstan. Innovative approaches are being taken to ensure the continuity of education and training. Thanks to the rapid response measures taken by governments and partners around the world to ensure the smooth learning process. The ongoing digital transformation of an educational institution requires appropriate information content, suitable methodological models, effective teaching methods and a supportive learning environment. The solution to one of the urgent tasks is to ensure the quality and reliability of assessing the knowledge of students by introducing an online proctoring system. The primary task of the online proctoring system is to recognize faces and identify abnormal behavior of students. The basis for obtaining data is the unified information educational environment of the D. Serikbayev East Kazakhstan Technical University is represented by the SPORTAL hardware and software system, which is an integration of two powerful subsystems: a Web application - the Dales of Knowledges educational portal and the SPORTAL information and software complex. The main theoretical results obtained are aimed at solving practical problems and are being introduced into the educational environment of D. Serikbayev East Kazakhstan Technical University to increase the degree of confidence in the results of students’ knowledge in distance learning using an online proctoring system. The article presents the results of studies of one of the Viola-Jones face detection methods, commonly known as Haar cascades. During the study, a technology for identifying faces and detecting violations in real time was developed. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted.

61-73

Authors: K. Asmaganbetova, Zhenis Otarbay, Almaz Turginbekov

The efficacy of the organization and corporate strategy are substantially impacted by enterprise resource planning (ERP) technologies. However, it might be difficult to manage and apply the anticipated benefits of ERP. Workarounds further complicate outdated and evolving business procedures, which impedes the ongoing development of ERP. As a result, reaping the rewards is sometimes challenging. The complexity of realizing advantages also rises when subsidiaries are not able to discover new benefits on their own, even though benefits may differ from subsidiary to subsidiary. ERP systems, which integrate, synchronize, and centralize corporate data, are frequently viewed as a key resource for businesses that thrive in a fastevolving global market. The selection of ERP systems is a crucial and challenging strategic choice because of the high cost of purchasing, installation, and implementation as well as the variety of offers. Because there are many different tangible and intangible requirements. This paper presents data on the development of innovative digital technologies in enterprise management. The main theoretical concepts of innovative digital technologies are described, as well as automated control systems and methods of their development are studied. This paper presents a model of innovation management in an industrial enterprise, an analysis of the ICT market, and developed recommendations on the methodology for implementing an ERP (Enterprise Resource Planning) system.

87-117

Authors: A. Biloshchytskyi, A. Kuchansky, S. Biloshchytska, Y. Andrashko, S. Toxanov, A. Faizullin

The article discusses the creation of an information-analytical system for evaluating the scientific performance of structural units of universities and research institutes based on the approach of constructing complex integral evaluation. A model of information technology for evaluating the results of scientific activity is proposed, consisting of four modules: an information collection module, an information storage module, an analytical module, and a module for user interaction and data visualization. The modular structure of the technology will allow expanding and modifying the capabilities of each of the modules independently of the others, as well as increasing the stability and flexibility of the technology. The implementation of this system is performed using microservices technology. A conceptual model of the information system and a structural model of the functioning of the information collection module, as well as a structural model of the information system database, are proposed. It is shown that most of the well-known indices for evaluating the performance of subjects of scientific activity, for example, h-index, g-index, e-index, I-10 index, etc., do not fully take into account information about citation. Therefore, the method for calculating the evaluation of scientific research activities of scientists was proposed, which does not lose information about any citation of the author and publication. This method determines the scalar evaluation of the results of scientific activity, and it is based on determining a few coefficients. The coefficients define one scientist’s citation in the publications of other scientists. As a result, assessment is obtained by solving a system of linear algebraic equations that are constructed based on calculated coefficients. Most of the known evaluation approaches have their own calculation features and disadvantages, which are associated with the loss of some information. Therefore, it is not recommended to give preference to one of them. For the purposes of a comprehensive assessment of the productivity of research activities of scientists, the authors proposed a method of vector evaluation of the results and the construction of the integral assessment. This method is based on the construction of vectors and scalar estimates for each scientist in a multidimensional metric space. The dimensionality of the space is determined by the number of calculated scalar estimates. The method is also based on the construction of an ideal point, which consists of scalar estimates that are the best in terms of achieving maximum performance. The assessment of each subject of scientific activity is calculated as the metric distance from the ideal point to the vector of scalar estimates of this subject of scientific activity.

22-32

Authors: D. Muratuly, N.F. Denissova, Y.V. Krak, К.S. Apayev

This article considers the relevant problem of biometric authentication of students in higher educational institutions. The authors present the results of using a turnstile system with a face recognition terminal, with the ability to provide unique biometric data in real time. The study was conducted among students of the D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan. The article presents the results of studies of one of the biometric methods of personality recognition. In this method, the process of proving and verifying the identity of the person can be carried out through the presentation by the user of his biometric image. The processing results are sorted and compared with typical images from the database. With its positive decision, the developed software issues the results of biometric authentication of a person who presented himself in front of a digital scanner. The applied value of the results of the work lies in the possibility of using them in the field of education, and various industries to make a decision on providing access to information resources. In the course of the study, a technology was developed to provide biometric authentication processes for university students. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted. A review of biometric methods of recognition is carried out, and tools for electronic authentication and modern information security systems are described. Factors that significantly affect the probability of correct recognition of students’ faces are determined. The analysis of ways to increase the probability of correct recognition of students by the image of the face is carried out.

45-60

Authors: Shyngys Akhmetbek

The ability to forecast customers’ future purchases, lifetime value, and churn are fundamental tasks in business management. These tasks become more complicated when the relationship between customers and business is not contractual. Therefore, the application of an appropriate method of customer analysis influences the efficiency of company cost management in interaction with their customers. The purpose of this paper is to compare existing solutions of customer lifetime value prediction and provide a new way to predict the future behavior of customers with consideration of the drawbacks of previous works. The method should have the following properties: use data that is available in any retail business; take into account that markets are constantly changing; be more precise than existing solutions. In this paper, we proposed the method of identifying customer churn provided a way to analyze customer behavior associated with churn or retention. In order to understand why customers churn, we used eleven customer behavioral metrics. The relationship of used metrics with churn was proved using churn cohort analysis. The results of training of logistic regression and neural network on prepared dataset showed that their forecast accuracy is in the healthy range for highly predictable churn. Based on predicted churn probabilities, we calculated the customer lifetime value in the future period. Our research results on customer behavior in the retail business confirm the hypothesis that customers who make many purchases are less likely to churn than customers who make few purchases. The main uniqueness of this work is the way of finding customer churn, as no such data was provided in the initial dataset. In addition, the minimum amount of data that most retail companies have was used. This enables the proposed methodologies to be applied to a large number of retail companies.

68-78

Authors: D. Nazyrova, Z. Aitkozha

The work is devoted to the study of Landsat-8 multispectral images of not high resolution using the spectral angle method on the base of spectral signatures libraries to detect objects under construction in an urban area. The physical basis of the research method is that all objects have different reflection coefficients depending on the wavelength. This property makes it possible to identify various substances by their spectral signatures. In the work, an automatic comparison of the curves of the spectral reflectivity of objects on a lowresolution space multispectral image was made to identify the identity of the characteristic energy absorption and reflection zones for detecting objects in the construction process. The article also describes the stages of image preprocessing, cross-track illumination correction of the image, atmospheric correction, and mathematical operations of bands transformation, which provide more opportunities for analysis and recognition of objects using a spectral study of a space image. The study accurately determines the presence or absence of the desired materials, since the search is based on the molecular structure of the substance. Also, the use of multispectral images allows you to analyze the entire city at the same time. The initial data was taken from a 2021 Landsat-8 satellite image with 11 bands, with a resolution of 30 meters, which was enhanced to 15 meters during pre-processing. The results of the search and detection of objects under construction in the city are given. The detection results can be used as input data for further in-depth analysis.

89-96

Authors: A. Mussina, S. Aubakirov, P. Trigo

The growth of data in social networks facilitate demand for data analysis. The field of event detection is of increasing interest to researchers. Events from real life are actively discussed in the virtual space. Event detection results can be used in a variety of applications, from digital marketing to collecting data about natural disasters. Thereby, researchers face the emergence of new algorithms along with the improvement of existing solutions in the event detection field. This paper proposes improvements to the SEDTWik (Segment-based Event Detection from Tweets using Wikipedia) algorithm. The SEDTWik algorithm is designed to detect events without contextual guidance. The overall SEDTWik detection process excludes the perspective of a topic, or multi-topic, guided (or semi-supervised) event detection approach. As a result, some interesting narrowly focused events are not detected as they are weakly relevant in a broader context (e.g., Wikipedia) although acquiring relevance within a conditioned context. Therefore, there is a need for an adaptive perspective where data is to be analysed against a set of narrower topics of interest. This paper shows that SEDTWik gains expressive power after being extended with multi-topic semi-supervision. The evaluation of the current proposal uses the well-known corpora with labeled events, Events2012. In the Events2012 dataset used notation category for events, meaning that events are combined by a certain topic. SEDTWik with topic dictionaries was checked across all categories. In the main part of the article, it is also explained the process of topic dictionary construction from Events2012 labeled tweets. At this stage of the research, in all tasks unigrams were used. SEDTWik with dictionaries showed improved accuracy, and more events were found within a certain category.

107-118

Authors: B. Imankulova, S. Alpar, S. Amanzholova

The article describes the IoT infrastructure, the hardware of the IoT system, considers the issue of security of the chosen LoRa data transmission technology. Data was received from sensors for gas, temperature and humidity, atmospheric pressure, as well as the location of the end device. At the same time, the standardized security features of the selected LoRa technology for transmitting data from sensors to the server were investigated. The article deals with LoRa bi-directional secure communication line, the security function requires devices/end devices to be configured through the LoRa gateway. Security research is devoted to the development of a security mechanism to increase its resilience. The payload was formed with a hash of the last bytes, and the entire payload was encrypted with AES for integrity and confidentiality. A method for assessing and visualizing atmospheric air pollution is given on the example of the city of Almaty, Kazakhstan. The process of numerical modeling of the study of emissions of harmful substances into the atmosphere is based on a mathematical model formed by the system of Navier-Stokes equations, consisting of the continuity equation, as well as the equations of motion and the k-epsilon turbulence model. To test the numerical methods for processing mixing and chemical reactions, a test problem was chosen – a jet in a transverse flow. Three-dimensional numerical simulation has been implemented. The use of the Internet of Things (IoT) and the acquisition of big data made it possible to simultaneously observe the concentrations of several pollutants in the atmosphere, calculate this concentration and analyze the state of the surface air layer. Modeling allows forecasting the possible concentration of pollutants in certain areas at certain times of the year.