Models that describe the trace element status formation in the human organism are essential for a correction of micromineral (trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal mechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance. So, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in urine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equilibrium. Since many relationships in the system are not known, the use of standard mathematical models is difficult. The artificial neural network use is suitable for constructing a model in the best way because it can take into account all dependencies in the system implicitly and process inaccurate and incomplete data. We created several neural network models to describe the retentions of trace elements in the human body. On the model basis, we can calculate the microelement levels in the body, knowing the trace element levels in drinking water and urine. These results can be used in health care to provide the population with safe drinking water.

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This paper is devoted to the study of the construction and application of an artificial neural network for calculating the doses of fertilizer application at the planned yield level in the conditions of grey forest soils of the Republic of Tatarstan. When using mineral fertilizers for crops, it is crucial to comply with the norms and doses of the introduced substances. An overabundance of fertilizer leads to residual accumulation in the soil. Lack of quantity of applied doses affects the quality of the crop, as well as the fertility and ecological situation of agricultural lands. High modern technology and information tools allow solving this problem.

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This article describes the method for calculation of probability integral indicator of the level of air pollution. The calculation is based on information on easily identifiable meteorological parameters by using neural network technology. This is especially true in terms of space-time constraints for existing network stations for environmental monitoring system observations.