IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v18y2004i5p483-495.html
   My bibliography  Save this article

A Forecast Model of Hydrologic Single Element Medium and Long-Period Based on Rough Set Theory

Author

Listed:
  • Si-Hui Dong
  • Hui-Cheng Zhou
  • Hai-Jun Xu

Abstract

On the basis of rough set theory, this paper presents the single element medium- and long-term classification forecast model that uses historical data of a hydrologic series as forecast factors. The minimal rule set, i.e., forecast pattern set, is achieved according to the principle of maximal attribute significance and rules frequency. Maximal support strength is put forward and applied to predict by using the model. The model is applied to forecast annual runoff of Dahuofang reservoir. The result indicates that the forecast model based on rough set can describe the relationship between forecast factors and forecast object efficiently and accurately. This model, which is composed of simple solution rules, can be easily understood and applied. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Si-Hui Dong & Hui-Cheng Zhou & Hai-Jun Xu, 2004. "A Forecast Model of Hydrologic Single Element Medium and Long-Period Based on Rough Set Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 483-495, October.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:5:p:483-495
    DOI: 10.1023/B:WARM.0000049180.27315.12
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/B:WARM.0000049180.27315.12
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/B:WARM.0000049180.27315.12?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Waseem Boota & Chaode Yan & Tanveer Abbas & Ziwei Li & Ming Dou & Ayesha Yousaf, 2021. "Comparative study of flash flood in ungauged watershed with special emphasizing on rough set theory for handling the missing hydrological values," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1387-1405, November.
    2. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2683-2703, September.
    3. Maryam Zavareh & Viviana Maggioni, 2018. "Application of Rough Set Theory to Water Quality Analysis: A Case Study," Data, MDPI, vol. 3(4), pages 1-15, November.
    4. Yong-Ying Zhu & Hui-Cheng Zhou, 2009. "Rough Fuzzy Inference Model and its Application in Multi-factor Medium and Long-term Hydrological Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 493-507, February.
    5. Yonas Ghile & Roland Schulze, 2010. "Evaluation of Three Numerical Weather Prediction Models for Short and Medium Range Agrohydrological Applications," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 1005-1028, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nijkamp, Peter & Poot, Jacques, 2015. "Cultural Diversity: A Matter of Measurement," IZA Discussion Papers 8782, Institute of Labor Economics (IZA).
    2. Maurizio d’Amato, 2007. "Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies," International Real Estate Review, Global Social Science Institute, vol. 10(2), pages 42-65.
    3. Salvatore Barbagallo & Simona Consoli & Nello Pappalardo & Salvatore Greco & Santo Zimbone, 2006. "Discovering Reservoir Operating Rules by a Rough Set Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 19-36, February.
    4. Alessandro Scuderi & Luisa Sturiale & Giuseppe Timpanaro & Agata Matarazzo & Silvia Zingale & Paolo Guarnaccia, 2022. "A Model to Support Sustainable Resource Management in the “Etna River Valleys” Biosphere Reserve: The Dominance-Based Rough Set Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    5. Mi, Yunlong & Wang, Zongrun & Quan, Pei & Shi, Yong, 2024. "A semi-supervised concept-cognitive computing system for dynamic classification decision making with limited feedback information," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1123-1138.
    6. Gülgönül Bozoğlu Batı & İsmail Hakkı Armutlulu, 2020. "Work and family conflict analysis of female entrepreneurs in Turkey and classification with rough set theory," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-12, December.
    7. Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    8. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.
    9. Ching-Hsue Cheng & Ssu-Hsiang Wang, 2015. "A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1979-1994, December.
    10. Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    11. Peter Goodings Swartz & P Christopher Zegras, 2013. "Strategically Robust Urban Planning? A Demonstration of Concept," Environment and Planning B, , vol. 40(5), pages 829-845, October.
    12. Gift Dumedah & Nadine Schuurman, 2008. "Minimizing the effects of inaccurate sediment description in borehole data using rough sets and transition probability," Journal of Geographical Systems, Springer, vol. 10(3), pages 291-315, September.
    13. Suresh Dara & Haider Banka & Chandra Sekhara Rao Annavarapu, 2017. "A Rough Based Hybrid Binary PSO Algorithm for Flat Feature Selection and Classification in Gene Expression Data," Annals of Data Science, Springer, vol. 4(3), pages 341-360, September.
    14. Sarah Ben Amor & Fateh Belaid & Ramzi Benkraiem & Boumediene Ramdani & Khaled Guesmi, 2023. "Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda," Annals of Operations Research, Springer, vol. 325(2), pages 771-793, June.
    15. Min-Hsiung Wei & Ching-Hsue Cheng & Chung-Shih Huang & Po-Chang Chiang, 2013. "Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1761-1779, April.
    16. Stelios Bekiros & Nikolaos Loukeris & Nikolaos Matsatsinis & Frank Bezzina, 2019. "Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 647-667, August.
    17. Weijie Wang & Wei Wang & Moon Namgung, 2010. "Linking People's Perceptions and Physical Components of Sidewalk Environments—An Application of Rough Sets Theory," Environment and Planning B, , vol. 37(2), pages 234-247, April.
    18. Baizhong Yan & Furong Yu & Xiao Xiao & Xinzhou Wang, 2019. "Groundwater quality evaluation using a classification model: a case study of Jilin City, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 735-751, November.
    19. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    20. Lutz, Lotte Marie & Fischer, Lisa-Britt & Newig, Jens & Lang, Daniel Johannes, 2017. "Driving factors for the regional implementation of renewable energy ‐ A multiple case study on the German energy transition," Energy Policy, Elsevier, vol. 105(C), pages 136-147.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:18:y:2004:i:5:p:483-495. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.