Ontological framework for optimal and sustainable allocation of water resources
Subject Areas : Research Paper
Mahdie Sadeghian
1
,
صبا صارمی نیا
2
*
1 - PHD Student, Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfaha
2 - Assistant Professor, Department of Industrial and Systems Engineering, Isfahan University of Technology
Keywords: Water management, Water resource allocation, Ontology, Knowledge graph,
Abstract :
Introduction: Water, as one of the fundamental and vital natural resources, plays an indispensable role in sustainable development and in meeting human, agricultural, and industrial needs. However, population growth, rapid urbanization, climate change, and the unequal distribution of water resources worldwide have created major challenges in managing and optimally allocating these resources.
Methods: In this study, a comprehensive ontology was developed with the aim of intelligent modeling and addressing key questions related to the identification and assessment of water resources, resource allocation and analysis, modeling and forecasting, and the use of technology and alternative water sources. The proposed ontology not only covers the technical aspects of water supply and demand but also enables structural analysis and provides answers to questions concerning allocation models, influencing factors, and optimization methods.
Findings: The developed ontology considers various types of water demand, water resources, quality of each source, factors influencing resource changes, allocation models and methods, sustainability indicators, and essential constraints and solution techniques. The results revealed that pollution strongly affects water quality, thereby altering the usability of water sources and influencing allocation decisions. Moreover, the quantitative evaluation of the ontology based on three common metrics—Relation Richness (RR=0.24), Attribute Richness (AR=0.38), and Inheritance Richness (IR=1.20)—indicates acceptable diversity in relations, moderate attribute coverage, and a balanced hierarchical structure. A review of previous studies further revealed that most focused on surface water allocation aimed at reducing shortage costs and often relied on linear models, while less attention was given to pollution and groundwater resources.
Conclusion: Queries applied to the ontology demonstrated that existing quantitative studies have addressed complex issues such as climate change, hydrogeology, and multi-objective modeling. Although simple models provide preliminary insights, incorporating more realistic factors such as pollution and environmental characteristics leads to more accurate and practical results.
1. Hellström D, Jeppsson U, Kärrman E. A framework for systems analysis of sustainable urban water management. Environmental impact assessment review. 2000;20(3):311-21.
2. Yuan L, Yang D, Wu X, He W, Kong Y, Ramsey TS, et al. Development of multidimensional water poverty in the Yangtze River Economic Belt, China. Journal of Environmental Management. 2023;325:116608.
3. Ghobadi A, Cheraghi M, Sobhanardakani S, Lorestani B, Merrikhpour H. Groundwater quality modeling using a novel hybrid data-intelligence model based on gray wolf optimization algorithm and multi-layer perceptron artificial neural network: a case study in Asadabad Plain, Hamedan, Iran. Environmental Science and Pollution Research. 2022:1-15.
4. Sun J, Li Y, Suo C, Liu J. Development of an uncertain water-food-energy nexus model for pursuing sustainable agricultural and electric productions. Agricultural Water Management. 2020;241:106384.
5. Mekonnen M, Hoekstra A. Sustainability: Four billion people facing severe water scarcity, Sci. Adv., 2, e1500323. 2016.
6. Parmesan C, Morecroft MD, Trisurat Y. Climate change 2022: Impacts, adaptation and vulnerability: GIEC; 2022.
7. Biswas AK. Integrated water resources management: a reassessment: a water forum contribution. Water international. 2004;29(2):248-56.
8. Zehtabian E, Masoudi R, Yazdandoost F, Sedghi-Asl M, Loáiciga HA. Investigation of water allocation using integrated water resource management approaches in the Zayandehroud River basin, Iran. Journal of Cleaner Production. 2023;395:136339.
9. Naeem K, Aloui S, Zghibi A, Mazzoni A, Triki C, Elomri A. A system dynamics approach to management of water resources in Qatar. Sustainable Production and Consumption. 2024;46:733-53.
10. Meng C, Zhou S, Li W. An Optimization model for water management under the dual constraints of water pollution and water scarcity in the Fenhe River Basin, North China. Sustainability. 2021;13(19):10835.
11. Homavandi H, Norouzi Y, Rashidi S. Applying Ontologies in Knowledge Management: A Systematic Review. Knowledge Retrieval and Semantic Systems. 2023;10(34):225-60.
12. Ospan A, Mansurova M, Barakhnin V, Nugumanova A, Titkov R. The development of a water resource monitoring ontology as a research tool for sustainable regional development. Data. 2023;8(11):162.
13. Smith B. Ontology. The furniture of the world: Brill; 2012. p. 47-68.
14. Noy NF, McGuinness DL. Ontology development 101: A guide to creating your first ontology. Stanford knowledge systems laboratory technical report KSL-01-05 and …; 2001.
15. Gruber TR. A translation approach to portable ontology specifications. Knowledge acquisition. 1993;5(2):199-220.
16. Gómez-Pérez A. Ontological engineering: A state of the art. Expert Update: Knowledge Based Systems and Applied Artificial Intelligence. 1999;2(3):33-43.
17. Drouin J, Gosset L, Wamejonengo J. Water Places: From Relational Ontologies. Water Policy in New Caledonia: Participative Co-building of Water Governance in a Decolonization Process. 2025;32:129.
18. Lukovac P, Joksimovic A, Bogdanovic Z, Despotovic-Zrakic M, Barac D. Modeling smart apiculture ecosystem: An ontology-based approach. 2025.
19. Keshavarzi M, Ghaffary HR. An ontology-driven framework for knowledge representation of digital extortion attacks. Computers in Human Behavior. 2023;139:107520.
20. Shahraki AS, Tash M, Caloiero T, Bazrafshan O. Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm. Sustainability. 2024;16(13):5801.
21. Yuan L, Wu X, He W, Degefu DM, Kong Y, Yang Y, et al. Utilizing the strategic concession behavior in a bargaining game for optimal allocation of water in a transboundary river basin during water bankruptcy. Environmental Impact Assessment Review. 2023;102:107162.
22. Park J, Bayraksan G. A multistage distributionally robust optimization approach to water allocation under climate uncertainty. European Journal of Operational Research. 2023;306(2):849-71.
23. Azari P, Sobhanardakani S, Cheraghi M, Lorestani B, Goodarzi A. A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran. Environmental Science and Pollution Research. 2024;31(17):26217-30.
24. Suo M, Xia F, Fan Y. A fuzzy-interval dynamic optimization model for regional water resources allocation under uncertainty. Sustainability. 2022;14(3):1096.
25. He W, Yang L, Li M, Meng C, Li Y. Application of an Interval Two-Stage Robust (ITSR) Optimization Model for Optimization of Water Resource Distribution in the Yinma River Basin, Jilin Province, China. Water. 2020;12(10):2910.
26. Alfaisal FM, Alam S, Alharbi RS, Kaur K, Khan MA, Athar MF, et al. Application of an optimization model for water supply chain using storage reservoir operation for efficient irrigation system. Discrete Dynamics in Nature and Society. 2023;2023(1):7932653.
27. Naghdi S, Bozorg-Haddad O, Khorsandi M, Chu X. Multi-objective optimization for allocation of surface water and groundwater resources. Science of the Total Environment. 2021;776:146026.
28. Zhou M, Sun D, Wang X, Ma Y, Cui Y, Wu L. Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios. Agricultural Water Management. 2024;302:109015.
29. Li Y, Han Y, Liu B, Li H, Du X, Wang Q, et al. Construction and application of a refined model for the optimal allocation of water resources—Taking Guantao County, China as an example. Ecological Indicators. 2023;146:109929.
30. Calvete HI, Galé C, Iranzo JA, Mateo PM. A decision tool based on bilevel optimization for the allocation of water resources in a hierarchical system. International Transactions in Operational Research. 2023;30(4):1673-702.
31. Ghobadi F, Kang D. Application of machine learning in water resources management: A systematic literature review. Water. 2023;15(4):620.
32. Studer R, Benjamins VR, Fensel D. Knowledge engineering: Principles and methods. Data & knowledge engineering. 1998;25(1-2):161-97.
33. Escobar P, Roldán-García MdM, Peral J, Candela G, Garcia-Nieto J. An ontology-based framework for publishing and exploiting linked open data: A use case on water resources management. Applied Sciences. 2020;10(3):779.
34. Bonacin R, Nabuco OF, Junior IP. Ontology models of the impacts of agriculture and climate changes on water resources: Scenarios on interoperability and information recovery. Future Generation Computer Systems. 2016;54:423-34.
35. Elag M, Goodall JL. An ontology for component‐based models of water resource systems. Water Resources Research. 2013;49(8):5077-91.
36. Li S, Erickson C, Zajac M, Guo X, Duan Q, Gong J. A Semi-Automated Framework for Flood Ontology Construction with an Application in Risk Communication. Water. 2025;17(19):2801.
37. Du W, Liu C, Xia Q, Wen M, Hu Y, Chen Z, et al. OFPO & KGFPO: Ontology and knowledge graph for flood process observation. Environmental Modelling & Software. 2025;185:106317.
38. نوذری. هستاننگاری فرادادهای پایاننامهها: طراحی یک الگو. مطالعات کتابداری و سازماندهی اطلاعات. 2024;35(1):75-122.
39. هادی تا, بیتا ش. PersianFarm: دیتاستی برای تطبیق آنتولوژی های فارسی.