Optimization of measures for the technical operation of irrigation systems by artificial intelligence methods

The purpose of the study is to develop a constructive solution for choosing priority objects of repair and restoration works using artificial intelligence methods in conditions of limited budget financing of operational measures of irrigation systems. The paper uses methods of system analysis and mathematical modeling, including binary variables for discrete optimization using evolutionary genetic programming methods. Based on the analysis of management decision support by means of mathematical support, the use of optimization methods for predicted impacts is justified, including the choice of discrete options to increase the functionality and effectiveness of planned technical operation measures. The allocation of limited financial resources is carried out on the model of multi-criteria optimization, including minimizing irrigation water losses, while increasing the area of irrigated land and increasing the financial indicators of the water management organization, which improves the quality of management impacts. The practical significance of research is determined by the development of innovative tools to solve the problem of allocating limited resources for carrying out repair and restoration work of the municipal water management complex using artificial intelligence methods. The approbation of the proposed solutions, carried out on the materials of the operation service of the State Budgetary Institution of the Republic of Crimea “Crimean Department of Water Management and Melioration”, proved the expediency of large-scale implementation of methods for quantitative assessment of management decisions. The technical and economic indicators of the planned activities correspond to the expected values and ensure the fulfillment of the following requirements: completeness, ensuring that they provide the necessary and sufficient information for decision-making; availability of reliable sources of reliable and accessible data for the information content of indicators and criteria.

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