No abstract

ПРОГНОЗНЕ МОДЕЛЮВАННЯ НЕЛІНІЙНИХ НЕСТАЦІОНАРНИХ ПРОЦЕСІВ У РОСЛИННИЦТВІ З ВИКОРИСТАННЯМ ІНСТРУМЕНТІВ SAS ENTERPRISE MINERBlackground. The issue of providing the increase of production of main agricultural crops in Ukraine under conditions of environmental management requires the use of modern scientific approaches. The complexity of solving this problem lies in the lack of practical experience of applying modern information-analytical systems, where different methods for analysis and modeling of nonlinear non-stationary processes in crop production would be implemented simultaneously. The proposed methodology has the advantage of using the tools of SAS Enterprise Miner -software where a wide range of techniques are implemented, that should be used for predictive modeling of main agricultural crops according to the performed research. Objective. The goal of the study is in application of the integrated methods of analysis and predictive modeling of non-stationary processes for agricultural crop yield prediction using SAS Enterprise Miner tools. Methods. To solve the problems stated the following approaches were used: systems analysis, regression analysis, gradient boosting, probabilistic modeling and decision trees. The methodology for developing of crop yield prediction under influence of various groups of factors was offered, and the possibility of their use in decision support systems in agriculture was substantiated. Results. Based on the analysis of the works of domestic and foreign scientists it was proposed to improve methodology of development of yield prediction of main agricultural crops using integrated analysis methods, which were implemented in the system of SAS Enterprise Miner. The analysis of the obtained results was performed. Conclusions. Winter wheat and corn yield prediction was performed for the Forest-Steppe Zone using the developed methodic. Different methods of construction of models for prediction of the non-stationary processes were applied; the choice of the worthiest one was reasonably proved. Advanced information technologies, including SAS Enterprise Miner, were used for automatization the process of selecting the optimal model for investigated crop yield prediction.

This paper addresses to the problem of using SAS Enterprise Guide 6.1 as a means for building probabilistic models and as optimum method of modeling gross domestic product in terms of the economic crisis and social threats is proposed. Today in a complex socio-political and economic situation growing influence of external factors, presence of uncertainties and risks there exists a problem of anticipating potential threats in the humanitarian and social spheres and ways to overcome them aiming to provide food security and controllability of ecological situation. All these problems, as reported in the NATO program "Science for Peace and Security", are of high priority for the countries that need to take into account threats to security, including Ukraine. That is why in the framework of the project NUKR. SFPP G4877 "Modeling and Mitigation of Social Disasters Caused by Catastrophes and Terrorism" the problems of scientific prediction of national economy for the period to 2030 as one of the measures preventing growth of social tension in the country are disclosed.