A given model of yield forecasting using an artificial neural network connects the wheat crop with the amount of productive moisture in the soil, soil fertility, weather, and factors in the presence of pests, diseases, and weeds. The difficulty of creating a yield forecast system is in the correct choice of predictors that have the greatest impact on yield. To build the model, moisture in the 100 cm layer of the soil, the content of nitrogen, phosphorus, humus, and soil acidity in the soil were used as input parameters. The amount of precipitation over 4 months, the average air temperature for the same period, as well as the presence of diseases, pests, and weeds were also taken into consideration. Data on 13 districts of the North Kazakhstan region in the period from 2008 to 2017 were used. The output parameter was the yield of spring wheat over the same time period. The relative importance of input variables in relation to the output variable was used to determine the weight values of input variables. An artificial neural network of error backpropagation was used as a method. The advantage of this method is that the quality of the forecast increases with a large amount of training data, as well as the ability to model nonlinear relationships between different data sources. After training the artificial neural network and obtaining predictive data, good results were achieved for predicting wheat yields (p=0.52, mean absolute error in percentage (MAPE)=12.02 %, root mean square error (RMSE)=3.368). Thus, it is assumed that the developed model for forecasting wheat yields based on data can be easily adapted for other crops and places and will allow the adoption of the right strategies to ensure food security

The presented paper is relevant as forecasting of crop yields is one of the main tasks of agricultural planning in any state. The purpose of the study is to assess the practical prospects of using a neural network system for forecasting crop yields in risky agricultural conditions at agricultural enterprises of the Republic of Kazakhstan. The basis of the methodological approach is a combination of quantitative and qualitative methods of investigating the prospects for the development and practical implementation of a neural network system for forecasting grain yield in the activities of agricultural enterprises of the North Kazakhstan region, using the MATLAB software suite that considers a number of key factors from the standpoint of the effectiveness of the described processes. The findings logically reflect the practical value of using a neural network system for forecasting grain yields in risky agricultural conditions and identifying the main factors influencing the accuracy of forecasting grain yields.

The Museum is a socially significant institution that is engaged in the identification, preservation, study and popularization of heritage. And tourism is becoming an integral part of modern society. Modern museums are designed to respond to the challenges of the time, become attractive to tourists and participate in the development of tourism business. The article emphasizes the special importance and necessity of cooperation between museums and tourism business. Based on the analysis of scientific literature, documents and statistical sources, the tourist resources of the museums of the Northern Territory are considered, the main problems of this sector and the ways of its further development are identified. Museum tourism at the present stage is considered as a promising direction.

The article highlights the historical events and phenomena associated with the North Kazakhstan region, the history of the Fatherland as the history of the state of a sovereign state, a new look, the course of development in conditions of freedom of thought, freed from class understanding. Includes data stored in the North-Kazakhstan State Archival and Library Fund, published in periodicals and not previously in scientific circulation.