IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i10d10.1007_s11269-024-03838-4.html
   My bibliography  Save this article

A Flood Season Division Model Considering Uncertainty and New Information Priority

Author

Listed:
  • Jun Li

    (Hainan University
    Yunnan Key Laboratory of Plateau Wetland Conservation, Restoration and Ecological Services)

Abstract

The seasonal temporal characteristics of runoff are crucial in regional water resources scheduling and administration, flood warning and forecasting, and efficient utilization of water resources through large-scale hub engineering scheduling. Traditional flood season division models are based primarily on consistency assumptions and deterministic indicators and can provide only a single division scheme. However, data consistency is greatly influenced by climate change and human activities, and indicators are becoming more uncertain. As a result, the consistency assumption of traditional flood season division models is unsatisfactory, and deterministic indicators cannot fully reflect the uncertainty of division indicators. Ultimately, the traditional flood season division model cannot adapt to the new form. In response to the above issues, the Flood Season Division Model based on the New Information Priority Principle - Interval Indicators (FSDMBNIP-II) is proposed. Following interval number theory, the IV-FSDI is calculated for both the training and validation sets. The IV-FSDI can represent the fluctuation of flood season division indicators through the interval radius to reflect the uncertainty of flood season division indicators. Optimization models are developed based on IV-FSDI for the two sets. NSGA-II is employed for solving optimization models, resulting in two Pareto solution sets. Two final flood season division schemes are selected for the two Pareto solution sets using the TOPSIS method. According to the new information priority principle, based on the matrix comparison method, the training and validation set are weighted, with more weight given to newer data. This allows final model to differentiate and learn from the information in new and old data to handle inconsistent data. Based on the weighting results, the two final flood season division schemes are combined. The performance of FSDMBNIP-II was validated using the control basin of the Wudongde hydropower station as the research area and compared with the performances of a model without considering the new information priority principle (MWCNIPP), a model without considering the interval of indicators (MWCII), and a set pair analysis method (SPA). The comparative results showed that, in the predetermined scenarios, when compared to MWCNIPP, FSDMBNIP-II was the optimal model for 66.7% of scenarios. Additionally, when compared to the MWCII and to SPA, FSDMBNIP-II was the optimal model for 100% of the scenarios. The results indicate that compared to the comparative model, FSDMBNIP-II can more effectively adapt to flood season division in situations with inconsistent data and nonstationary indicator values.

Suggested Citation

  • Jun Li, 2024. "A Flood Season Division Model Considering Uncertainty and New Information Priority," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3755-3784, August.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:10:d:10.1007_s11269-024-03838-4
    DOI: 10.1007/s11269-024-03838-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03838-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03838-4?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. Li, Cheng & Qi, Qi, 2024. "A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry," Energy, Elsevier, vol. 287(C).
    2. Yanbin Li & Yubo Li & Kai Feng & Kaiyuan Tian & Tongxuan Huang, 2023. "Dynamic Control of Flood Limited Water Levels for Parallel Reservoirs by Considering Forecast Period Uncertainty," Sustainability, MDPI, vol. 15(24), pages 1-22, December.
    3. Arbel, Ami & Vargas, Luis G., 1993. "Preference simulation and preference programming: robustness issues in priority derivation," European Journal of Operational Research, Elsevier, vol. 69(2), pages 200-209, September.
    4. Yani Lian & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 21-37, January.
    Full references (including those not matched with items on IDEAS)

    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. Mikhailov, L., 2004. "A fuzzy approach to deriving priorities from interval pairwise comparison judgements," European Journal of Operational Research, Elsevier, vol. 159(3), pages 687-704, December.
    2. Wang, Ying-Ming & Elhag, Taha M.S., 2007. "A goal programming method for obtaining interval weights from an interval comparison matrix," European Journal of Operational Research, Elsevier, vol. 177(1), pages 458-471, February.
    3. Mustajoki, Jyri & Hamalainen, Raimo P. & Lindstedt, Mats R.K., 2006. "Using intervals for global sensitivity and worst-case analyses in multiattribute value trees," European Journal of Operational Research, Elsevier, vol. 174(1), pages 278-292, October.
    4. Haines, Linda M., 1998. "A statistical approach to the analytic hierarchy process with interval judgements. (I). Distributions on feasible regions," European Journal of Operational Research, Elsevier, vol. 110(1), pages 112-125, October.
    5. Finan, J. S. & Hurley, W. J., 1999. "Transitive calibration of the AHP verbal scale," European Journal of Operational Research, Elsevier, vol. 112(2), pages 367-372, January.
    6. Mikhailov, L., 2002. "Fuzzy analytical approach to partnership selection in formation of virtual enterprises," Omega, Elsevier, vol. 30(5), pages 393-401, October.
    7. Hocine, Amine & Kouaissah, Noureddine, 2020. "XOR analytic hierarchy process and its application in the renewable energy sector," Omega, Elsevier, vol. 97(C).
    8. Lipovetsky, Stan & Tishler, Asher, 1999. "Interval estimation of priorities in the AHP," European Journal of Operational Research, Elsevier, vol. 114(1), pages 153-164, April.
    9. Islam, R. & Biswal, M. P. & Alam, S. S., 1997. "Preference programming and inconsistent interval judgments," European Journal of Operational Research, Elsevier, vol. 97(1), pages 53-62, February.
    10. Çağlayan-Akay, Ebru & Topal, Kadriye Hilal, 2024. "Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models," Energy, Elsevier, vol. 305(C).
    11. Conde, Eduardo & de la Paz Rivera Pérez, María, 2010. "A linear optimization problem to derive relative weights using an interval judgement matrix," European Journal of Operational Research, Elsevier, vol. 201(2), pages 537-544, March.
    12. Faramondi, Luca & Oliva, Gabriele & Setola, Roberto & Bozóki, Sándor, 2023. "Robustness to rank reversal in pairwise comparison matrices based on uncertainty bounds," European Journal of Operational Research, Elsevier, vol. 304(2), pages 676-688.
    13. Cohen, Sandra & Doumpos, Michael & Neofytou, Evi & Zopounidis, Constantin, 2012. "Assessing financial distress where bankruptcy is not an option: An alternative approach for local municipalities," European Journal of Operational Research, Elsevier, vol. 218(1), pages 270-279.
    14. Guo, Min & Yang, Jian-Bo & Chin, Kwai-Sang & Wang, Hongwei, 2007. "Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1294-1312, November.
    15. Zeshui Xu & Xiaoqiang Cai, 2014. "Deriving Weights from Interval Multiplicative Preference Relations in Group Decision Making," Group Decision and Negotiation, Springer, vol. 23(4), pages 695-713, July.
    16. Vetschera, Rudolf, 1996. "Multi-criteria agency theory," Discussion Papers, Series I 280, University of Konstanz, Department of Economics.
    17. Tom Pape, 2020. "Value of agreement in decision analysis: Concept, measures and application," Papers 2012.13816, arXiv.org.
    18. Maha Shabbir & Sohail Chand & Farhat Iqbal & Ozgur Kisi, 2024. "Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4179-4196, September.
    19. Köksalan, Murat & Büyükbasaran, Tayyar & Özpeynirci, Özgür & Wallenius, Jyrki, 2010. "A flexible approach to ranking with an application to MBA Programs," European Journal of Operational Research, Elsevier, vol. 201(2), pages 470-476, March.
    20. Mats Danielson & Love Ekenberg, 2017. "A Robustness Study of State-of-the-Art Surrogate Weights for MCDM," Group Decision and Negotiation, Springer, vol. 26(4), pages 677-691, July.

    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:38:y:2024:i:10:d:10.1007_s11269-024-03838-4. 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.