Russian Federation
Russian Federation
Russian Federation
Russian Federation
UDC 631.671
Improving water efficiency is closely linked with the use of innovative information and technologies to support management decisions in land reclamation projects. The aim of this study is to validate a promising approach for managing water use in inter-farm irrigation systems using artificial intelligence techniques and twin models. Methodological approaches to creating a unified and automated management system are based on the optimization of management decisions made in conditions of incomplete and uncertain information, as well as the necessity to process large volumes of data. The analysis of the theoretical and practical aspects of managerial decision-making has been conducted, and principles for the creation of an automated irrigation management system using artificial intelligence and dual organizational models have been formulated. Priority areas for enhancing the management impact have been identified, and a functional and structural design of the automated control system "Water Use" and algorithms to support management decisions have been developed. A system of models has been developed to optimize water distribution in conditions of water scarcity, and to forecast the technical status of water supply networks and infrastructure. It also manages the financial and economic performance of operations, as well as auxiliary and service functions. The composition and structure of the database and knowledge for the automated control system, "Water Use", have been determined. Automation and optimization of decision making based on diagnostic analysis of current issues, a range of possible solutions, and informational and technological support for specialists involved in preparation and decision making, will ensure enhanced efficiency and quality in control activities.
management, irrigation, water use, technology, digitalization, artificial intelligence
1. Alaimo, L.S., Maggino, F. (2020). Sustainable development goals indicators at territorial level: Conceptual and methodological issues – the Italian perspective. Social Indicators Research, vol. 147, pp. 383-419.
2. Agriculture’s connected future: How technology can yield new growth//McKiney. Available at: https://www.mckinsey.com/industries/agriculture/ourinsights/agricultures-connected-future-how-technology-can-yield-newgrowth (accessed: 02.12.2023).
3. Transforming Agriculture through Digital Technologies//Deloitte. Available at: https://www2.deloitte.com/content/dam/Deloitte/gr/Documents/consumer-businessgr_Transforming_Agricultu re _through_Digital_Technologies_noexp.pdf/ (accessed: 30.11.2022). https://www.fao.org3/i6583e/i6583e.pdf (accessed: 02.02.2023).
4. UNDESA (2015). The millennium development goals report 2015. UN, 75 p.
5. Xarvio™ Digital Farming Solutions. Xarvio. Available at: https://www.xarvio.com/global/en.html (accessed: 02.08.2023).
6. Product Overview. Smart farm systems. Available at: https://www.smartfarm.ag/products/automation-options (accessed: 07.12.2023).
7. Pole vozmozhnostey: cifrovye resheniya dlya sel'skogo hozyaystva / Rosteh. URL: https://rostec.ru/news/pole-vozmozhnostey-tsifrovye-resheniya-dlya-selskogo-khozyaystva/ (data obrascheniya: 16.01.2023).
8. Prikaz Ministerstva sel'skogo hozyaystva RF ot 31 iyulya 2020 g. № 438 «Ob utverzhdenii Pravil ekspluatacii meliorativnyh sistem i otdel'no raspolozhennyh gidrotehnicheskih sooruzheniy».
9. Verhova G.V., Akimov S.V. Modeli dlya cifrovyh dvoynikov prostranstvenno-raspredelennyh ob'ektov // Proektirovanie i obespechenie kachestva informacionnyh processov i sistem: sbornik dokladov Mezhdunarodnoy konferencii. SPb.: SPbGETU «LETI», 2022. S. 163-170.
10. Kurganova N.V., Filin M.A., Chernyaev D.S., Shaklein A.G., Namiot D.E. Vnedrenie cifrovyh dvoynikov kak odno iz klyuchevyh napravleniy cifrovizacii proizvodstva // International Journal of Open Information Technologies. 2019. T. 7. № 5. S.105-111.
