skip to main content
10.1145/3659211.3659257acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbdeimConference Proceedingsconference-collections
research-article

Generic Ontologies for Digital Watersheds

Published: 29 May 2024 Publication History

Abstract

Digital Watershed represents an effective strategy for addressing floods and mitigating their associated risks. However, a notable challenge lies in the absence of a universally applicable modeling approach for constructing digital watersheds. The wealth of data and knowledge about watersheds is currently managed in a fragmented manner, impeding a comprehensive and cohesive understanding of the subject. This paper addresses the fragmented control landscape in watershed management by introducing generic ontologies, including water conservancy object ontology, model ontology, rainfall and runoff scene-mode ontology, and event ontology. These ontologies standardize the representation of water conservancy objects, hydrological models, and expert knowledge while also defining structured representations for physical events, scheduling rules, and business processes, which contribute to breaking the paradigm of "one watershed, one system" and facilitate integrated flood prediction and scheduling.

References

[1]
W Li, Y Zhai. Concept, Key Technologies and Challenges of Digital Twin Riverbasin, in Proceedings of the 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC), IEEE, 2022, pp. 117-122.
[2]
Ghaith M, Yosri A, El-Dakhakhni W. Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning, Water, vol. 14, no. 22, 2022, pp. 3619.
[3]
Y Li. Urban Flood Disaster Assessment and Early Warning System Analysis Based on Digital Twin Technology, Journal of Beijing University of Technology, vol. 48, no. 5, 2022.
[4]
Y Huang, S Yu, B Luo, Exploring Digital Twin for Integrated Intelligent Scheduling of Watershed Hydraulic Disaster Prevention in the Yangtze River, Journal of Hydraulic Engineering, vol. 53, no. 3, 2022, pp. 253–269.
[5]
Henriksen H J, Schneider R, Koch J, A New Digital Twin for Climate Change Adaptation, Water, vol. 15, no. 1, 2022, p. 25.
[6]
Lu J, Wang G, Törngren M. Design ontology in a case study for cosimulation in a model-based systems engineering tool-chain, IEEE Systems Journal, vol. 14, no. 1, 2019, pp. 1297-1308.
[7]
Yi S, Sun Y. Upper level ontology and integration assessment modeling in digital watershed, in Proceedings of the 2013 21st International Conference on Geoinformatics, IEEE, 2013, pp. 1-6.
[8]
J Feng, X Xu, J Lu. Construction and Application of Water Conservancy Information Knowledge Graph, Computer and Modernization, 2019, no. 9, pp. 35–40.M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.
[9]
Agresta A, Fattoruso G, Pollino M, An ontology framework for flooding forecasting, in Computational Science and Its Applications-ICCSA 2014: 14th International Conference, Guimarães, Portugal, June 30–July 3, 2014, Proceedings, Part IV 14, Springer International Publishing, 2014, pp. 417-428.
[10]
Y Chen, B Zou, W Niu, Research on Several Key Technologies of Integrated Basin Flood Forecasting and Scheduling System [J]. People's Yangtze, 2019, 50(07).
[11]
H Cheng. Flood Forecasting System and Its Role in Engineering Scheduling [J]. China Water Resources, 2020, (17).

Index Terms

  1. Generic Ontologies for Digital Watersheds

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    BDEIM '23: Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management
    December 2023
    917 pages
    ISBN:9798400716669
    DOI:10.1145/3659211
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 May 2024

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    BDEIM 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 4
      Total Downloads
    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 15 Oct 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media