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ISSN 2227-6017 (ONLINE), ISSN 2303-9868 (PRINT), DOI: 10.18454/IRJ.2227-6017
ПИ № ФС 77 - 51217

DOI: https://doi.org/10.23670/IRJ.2017.60.109

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Бронникова М. В. ПОДХОДЫ К МОДЕЛИРОВАНИЮ АВТОМАТИЗИРОВАННЫХ СИСТЕМ ИНТЕЛЛЕКТУАЛЬНОГО МОНИТОРИНГА И УПРАВЛЕНИЯ ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТЬЮ В СФЕРЕ ГИДРОСЕТИ / М. В. Бронникова // Международный научно-исследовательский журнал. — 2017. — № 06 (60) Часть 3. — С. 97—101. — URL: https://research-journal.org/technical/approaches-to-modeling-automated-systems-of-intellegent-monitoring-and-management-of-ecological-safety-in-drainage-system/ (дата обращения: 21.07.2017. ). doi: 10.23670/IRJ.2017.60.109
Бронникова М. В. ПОДХОДЫ К МОДЕЛИРОВАНИЮ АВТОМАТИЗИРОВАННЫХ СИСТЕМ ИНТЕЛЛЕКТУАЛЬНОГО МОНИТОРИНГА И УПРАВЛЕНИЯ ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТЬЮ В СФЕРЕ ГИДРОСЕТИ / М. В. Бронникова // Международный научно-исследовательский журнал. — 2017. — № 06 (60) Часть 3. — С. 97—101. doi: 10.23670/IRJ.2017.60.109

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ПОДХОДЫ К МОДЕЛИРОВАНИЮ АВТОМАТИЗИРОВАННЫХ СИСТЕМ ИНТЕЛЛЕКТУАЛЬНОГО МОНИТОРИНГА И УПРАВЛЕНИЯ ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТЬЮ В СФЕРЕ ГИДРОСЕТИ

Бронникова М.В.

Аспирант 3 курса института инженерных технологий и естественных наук

Белгородский государственный национальный исследовательский университет

ПОДХОДЫ К МОДЕЛИРОВАНИЮ АВТОМАТИЗИРОВАННЫХ СИСТЕМ ИНТЕЛЛЕКТУАЛЬНОГО МОНИТОРИНГА И УПРАВЛЕНИЯ ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТЬЮ В СФЕРЕ ГИДРОСЕТИ

Аннотация

В статье предоставлен обзор наиболее популярных подходов к моделированию автоматизированных систем интеллектуального мониторинга и управления экологической безопасностью в сфере водохозяйственного сектора, выполненный на основе литературных источников, публикаций, представленных в отечественных и зарубежных научных журналах и открытых источниках. Описаны принципы построения адаптивных систем рассматриваемого класса и предъявляемые к ним требования. Представлена информация о ведущих российских и зарубежных центрах и университетах, имеющих значимые наработки в области моделирования систем интеллектуального экомониторинга в России.

Ключевые слова: модель системы, автоматизированная система интеллектуального мониторинга и управления, экологическая безопасность.

 

Bronnikova M.V.

Postgraduate student of the 3d course the institute of Engineering Technology and Science

Belgorod State National Research University, Belgorod, Russian Federation

APPROACHES TO MODELING AUTOMATED SYSTEMS OF INTELLEGENT MONITORING
AND MANAGEMENT OF ECOLOGICAL SAFETY IN DRAINAGE SYSTEM

Abstract

The article provides an overview of the most popular approaches to the modeling of automated systems for intelligent monitoring and management of environmental safety in the water sector, made on the basis of literature, publications, presented in national and international scientific journals and public sources. It also provides the principles of construction of adaptive systems of this class and requirements to them. The information on leading Russian and foreign centers and universities with significant experience in the field of predictive modeling systems of environmental intelligent monitoring in Russia is also represented in the article.

Keywords: model of the system, the automated system of intelligent monitoring and control, environmental safety.

The construction of modern systems in the field of environmental safety management should ensure that decision-making in a dynamically changing situation in fuzzy or incomplete information on the level of human intelligence. To do this, the components of the automated control systems should operate on the basis of adaptive models, the ability to adapt its structure and parameters to the time-varying states of object management and environmental conditions, the totality of which is the intelligent decision support components.

Analysis and review of the scientific literature in the study subject area revealed that the authors [5, P. 97], [7, P. 114] most fully reflect the basic principles of adaptive environmental safety management systems:

priority of the protection of the environment, life and health, as well as staff man-made objects in front of other commercial and social goals and objectives;

– compliance with the goals of the modeled system (all of its subsystems) targets higher systems;

– the completeness and adequacy of the collected and the resulting ecological information functioning of the system;

– universality of the model of environmental safety monitoring and control system for areas at various levels of the hierarchy and destination;

– adaptability and mobility, ensuring prompt response of the system and all of its constituent sub-systems to the current changes in the management of the facility and the environment;

– identification of causal relationships and regularities of functioning of pollution sources and their impact on the environment;

– realism of evaluations (forecasts).

Currently, the traditional mathematical models and methods do not ensure the implementation of the above mentioned principles in the decision support systems, as in a number of decision-making problems is very complex dependencies do not allow conventional analytical representation or due to incomplete information remain unknown, some elements of the model that determines the need for the use of computer simulation and artificial intelligence methods.

Well-built model, satisfying certain common requirements should be [9, P. 136], [10, P. 25]:

– functionally complete in terms of features to solve the main problems;

– adequate (able to render  the necessary completeness and accuracy of all object properties that are relevant for the purposes of this study);

– simple enough;

– easy to manage and use;

– adaptive (makes it easy to switch to other modifications or update data);

– changes are allowed (it can be complicated in operating process).

In the study of ecological systems homomorphic, less isomorphic, mathematical models of different levels of modeling are used. The most comprehensive assessment model described the quality of the aquatic environment and the propagation of pollutants; environmental and economic activities of enterprises; valuation of anthropogenic impact on water bodies, such as [8, P. 59], [11, P. 170], [13, P. 316].

Objects of water complex is a multi-dimensional system with a number of input and output variables, depending on the number which distinguishes the type of “one input – multiple output” of the system (SIMO), «a lot of inputs – one output» (MISO) and “Multiple Input – Multiple Outputs » (MIMO). They are described as “white”, “black” or “grey” boxes. “White” box, as known to take place in the system of processes, takes into account the specific form of the transfer functions of the individual blocks. In solving the problems of identifying transfer functions themselves “black” boxes are unknown and in this case the models are built on the basis of the empirical data and known mathematical relationships between input and output, with other ratios are used to a different set of data. This lack of experimental information about the system under study leads to errors in mathematical modeling, so in most cases used in practice mixed “grey” boxes.

Easy to use, a relatively small number of the initial information for the preliminary forecasts required in the development of control actions become causes of the spread of empirical models of the water sector, the construction of which are widely used methods of regression, variance, correlation, discriminate and cluster analysis.

However, the impossibility of taking into account the environmental hypotheses, causal relationships between variables and applicability only received conditions do not allow empirical models reveal the mechanisms of the phenomena studied, will be questionable validity forecast estimates obtained by applying these models in terms of extrapolation.

In order to rationalize the proposed method is a flexible information modeling [2, P. 114], in which, in contrast to the analytical approach to the “synthetic” black box method is modeled external functioning of the system. Let x to be a set, the components of which correspond to the quantitative properties of natural-technical system, x’is a lot of external influences that affect the result of the interaction of water and man-made systems. The response process can be described by an unknown vector function F: y = F (x, x’), where y is the response vector. The Information modeling is the identification of the test process consists in finding algorithm, the functional relationship or rule systems in general form ȳ = G (x, x’), associating each pair of vectors (x, x’) with the vector ȳ so that y and ȳ close to some metric that reflects the purpose of the simulation. Ratio ȳ is called the information model of the system F.

Without the use of mathematical modeling as an effective method of system analysis cannot account for all the important elements of the drainage system and its components, as well as numerous responses of aquatic ecosystems due to the high dynamics of the hydrosphere. Since it is difficult and often unacceptable conduct direct experiments with natural objects and the possibility of laboratory simulation is very limited, this method allows us to study the environmental situation is a real-time scale.

An important feature of the aquatic ecosystem modeling is that the mathematical description of the basic processes occurring in them is based on the laws of the various natural sciences: physical, chemical, hydrological, etc. and it is a prerequisite for the use of mathematical methods that reflect the exact scheme of causality.

Most widespread approach to describe physical and chemical processes, diffusion and transfer of substances in the water flow of the system of differential equations in partial derivatives, including the Navier-Stokes equations, turbulent diffusion (hydrodynamic component), the equations of motion (Saint-Venant), the equation of advection-diffusion, and description of processes as phase transfer phenomena transport substance (volatility and aeration) with two membrane model; assumptions on Gaussian distribution law; use Student’s test as a statistic. In models of eutrophication, the transfer from distributed sources, distribution of toxicants in aquatic environment are important sorption processes described by isotherms of Langmuir and Frendlich.

The complex nature of biological and microbiological processes causes their description homomorphic mathematical models. For example, processes associated with the decomposition of biomass production processes and described by simple material balance equations which are quite sensitive to external influences.

Special attention is paid to ecological modeling equations of chemical kinetics, many chemical reactions are considered to be a first-order reaction, to describe the relations between the ion and its concentration using a simplified equation of Debye-Hyukel. Suffice fast ionization processes, aggregation and precipitation included in the model as well-established limits the settling of suspended particles is always described by a differential equation of the first order.

Also, most fully developed hydrological models of water regime used in solving the problems of water management planning and hydrological forecasting related to the calculations and forecasts of the volume flow, the maximum cost impact assessment of human impact on the hydrological regime of the watersheds, as well as employees of the base to form the removal of models of dissolved and adsorbed substances, water erosion, silts, drains.

Optimization models have been widely used in solving various water sector management tasks: choosing the best water treatment schemes; calculation of reservoir dispatching management rules, as well as floods and floods; meet the demand in water consumption in cross-border areas; calculation of enterprise environmental charges and others.

Currently in the study of complex systems in cases where the analytical solution is difficult or impossible, simulations are gaining more and more popularity. Unlike mathematical models are the most adequate to the real ecological processes, the construction of which should be taken into account as much as possible the details, it is not always possible due to almost unattainable completeness of the information.

None of the studied system factors can be considered independent, as it constantly interacts with other and after a while begins to feel the effects of feedback. In today’s modeling the interaction of environmental and economic elements of the system is in the form of a cognitive map, the final balance which allows approximately assesses the type of feedback in the model constructed.

The integration of management information systems with artificial intelligence systems increase the efficiency of management through the timely provision of the person making the decision timely information required. The foreign and domestic research intellectualization of decision support has evolved in different directions of artificial intelligence.

Most fully in the research works of authors, for example, [6, P. 8], [12, P. 3], [14, P. 849] described the system, the main mechanism of intellectualization which is a particular way of reasoning, where the formation of decision support is based on rules, precedents, ontologies, knowledge is represented by a production and logic models, frames, semantic networks, precedents of problematic situations.

The prospects of intellectualization of the components of the control system based on smart technologies that mimic natural processes: fuzzy logic, evolutionary (genetic) algorithms and artificial neural network is shown [4, P. 114], [16, P. 42].

Common to all the genetic algorithm approach is based on the analogy of the dominance of Pareto with the process of natural selection (selection) in the process of evolution. Stability of genetic algorithms, parallel processing using a variety of alternative solutions to environmental problems and organizing the search for the most promising of them provide inherent properties of them use a minimum of information about the problem, operations on populations, encoding parameters, and randomization.

In the case of complex non-linear functions, when it is difficult to formalize the laws that bind the original input data and the output or impossible to construct an algorithm or a logical calculus of the complexity of the accounting of all the factors used neural network model. However, for the practical implementation of selected neural networks with a simple architecture, which is due to cost a lot of time to organize their structures and problems of retraining. For example, a network that has the structure of a multilayer perceptron with a direct signal transmission and is characterized by the most sustainable behavior, as well as network by Kohonen, Hopfield and others.

For the automation of complex, poorly structured processes in the works of authors such as [3, P. 5], [15, P. 72], considered the problems of using fuzzy information and fuzzy output as fuzzy modeling allows the management in the field of drainage into account not only quantitative but also qualitative factors: economic and geographic conditions of the region in which the water bodies, river basin districts, the sources of water supply, etc.

A significant expansion of the prospects for the development and practical application of mathematical modeling, neural networks, evolutionary algorithms in the field of drainage control associated with the use of geoinformation technologies [1, P. 21], intelligent components which allow us to automate the procedure of the hydrological regime of territories and fundamental laws of transferring substances; conduct space-time analysis; to solve the problems of planning transportation routes and transfer the water to the end user, ensuring the rational use of water resources, and others.

The world leader in the development and application of software and computer technology to hydrology, hydraulic engineering, water supply, sewerage and drainage, hydrogeology, oceanography and protection of the aquatic environment is a Danish company DHI: Water. Environment. Health [17]. Also significant results in the field of ecological modeling has made the US company Aquaveo [18], which produces software systems for managing groundwater and surface water.

In the Russian Federation, Institute of Limnology of the Russian Academy of Sciences (RAS) [19], Institute of Water Problems of the North Karelian Research Centre of Russian Academy of Sciences [20], St. Petersburg Economics and Mathematics Institute of Russian Academy of Sciences [21], Institute of Water and Ecological Problems, Siberian Branch of the Russian Academy of Sciences [22] actively engaged in the development models of intelligent systems to monitor and control the environmental safety of water management complex. The results of studies aimed at assessing and forecast changes in the hydrological regime, aquatic ecosystems in the development of the economy and climate change; estimate the distribution parameters of runoff and nutrient load on water bodies, etc. It should be noted that domestic products are embedded not only in our country but also in organizations around the world: USA, Canada, Germany, France and others.

The analysis showed that the existing automated systems of ecological safety of environmental monitoring and control in the field of drainage system – including having a well-developed network of mobile and fixed stations equipped with sensors and aquatic ecosystems monitoring devices that use aerospace methods of monitoring and GIS technologies, the calculated methods – are the following drawbacks that prevent the improvement of ecological situation: the lack of a common information space for environmental monitoring; weak implementation of strategic management; the absence of a comprehensive evaluation of the current projected state and the provision of ecological situation scenario development; weak updated decision support tasks.

To solve the above mentioned problems is needed to build and update the environmental safety management systems in the area of the drainage system, the functioning of which is based on a data mining with the use of situational modeling, which will provide an immediate transformation in specific productive ecological data into control actions.

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