Research on a cloud model intelligent computing platform for water resource management

• Published in: Journal of hydroinformatics Vol. 26; no. 11; pp. 2902 - 2927
• Main Authors: Wang, Tao, Duan, Jingjing, Zhai, Jiaqi, Zhao, Jing, Gao, Yulong, Gao, Feng, Zhang, Longlong, Zhao, Yifei
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.11.2024
• Subjects: Artificial intelligence; Big Data; Cloud computing; Fluid mechanics; Hydraulics; Hydrologic cycle; Hydrological cycle; Hydrology; Information management; Information systems; Internet of Things; Management information systems; Multiple objective analysis; Optimization models; Pattern analysis; Regional development; Regional planning; Resource allocation; Resource management; River basin development; River basins; Rivers; Simulation; Software; Technology; Water demand; Water management; Water resources; Water resources management; Water shortages; Watersheds; Weihe River
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2024.223

As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references for intelligent water management and refined allocation of water resources in the Weihe River Basin.