MATHEMATICAL FOUNDATIONS OF ALGORITHMIZATION OF WATER POLLUTION MODELING PROCESSES
Moldasheva R.N. Shazhdekeyeva N.K. Myrzagereikyzy G. Makhatova V.E. Zadagali A.M.
2023National Academy of Sciences of the Republic of Kazakhstan
News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences
2023#2023Issue 3164 - 179 pp.
The paper considers the actual task of developing mathematical foundations for algorithmization of the processes of modeling pollution of reservoirs. In the course of long - term studies of the distribution of phytoplankton of the Kokshetau lakes group, in particular, Lakes Zerendi, Kopa, Shalkar, Imantau, measurements of chemical parameters of water, organoleptic properties, transparency were carried out. These data were used to detail individual results and construct forecast values that depend on fluctuations in indicators that characterize the state of hydrobiota. In modeling, a lake is considered as a complex system, and surface sampling points are considered as sources of information about the state of a water body at certain time intervals. The solution of the task is carried out by constructing a critical area, and the incoming information is ranked by the level of significance. The hypothesis is the statement that a certain forecast value is accepted if it enters a certain critical area limited by the values that are determined as a result of experimental measurements. The advantage of the proposed approach is the possibility of simultaneous comparison of the influence of many factors, as well as the use of both empirical and theoretical frequencies. This approach to algorithmization of reservoir pollution modeling can be used to solve applied problems related to the development of ranking algorithms, assessing the impact of a number of factors on the management object, creating environmental monitoring programs near potentially hazardous and hazardous industrial facilities, creating information technologies for analysis and project activities.
competing hypothesis , concordance coefficient , critical domain , empirical frequency , interval , normalization , rank , theoretical frequency , time interval
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S. Seifullin Kazakh Agro Technical Research University, Astana, Kazakhstan
S. Seifullin Kazakh Agro Technical Research University
10 лет помогаем публиковать статьи Международный издатель
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