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A Novel Fuzzified Markov Chain Approach to Model Monthly River Discharge

Author

Listed:
  • Mohammad Mahdi Dorafshan

    (Isfahan University of Technology)

  • Mohammad Hossein Golmohammadi

    (Isfahan University of Technology)

  • Keyvan Asghari

    (Isfahan University of Technology)

  • Carlo Michele

    (Politecnico di Milano)

Abstract

River discharge is a hydrological variable resulting from integrated processes at the basin scale, which proves challenging to be modeled due to the partial knowledge of forcings and basin characteristics. In this context, the Traditional Markov chain Model (hereafter TMM) has been extensively used in discharge modeling over the past few decades. This study addresses the key weaknesses of the TMM and enhances it using fuzzy logic, introducing the novel Fuzzy Markov Chain Model (FMM). This model integrates traditional formulas with concepts related to fuzzy membership functions (MFs). The effectiveness of FMM is investigated through two case studies: an example taken from the Literature, featuring eight cases, highlighting differences between TMM and FMM, and the case study of the monthly inflows to Zayandehrud Dam (IZD) in Isfahan, Iran. The results from the Literature example show clear distinctions between TMM and FMM. In the IZD case, the future condition, as predicted by the FMM method with a maximum probability of 0.53, indicates a moderate wet condition. In contrast, the TMM assigns an equal probability (0.33) to all three conditions (low, moderate, and high), highlighting the TMM’s inefficiency in modeling probabilities across different conditions.

Suggested Citation

  • Mohammad Mahdi Dorafshan & Mohammad Hossein Golmohammadi & Keyvan Asghari & Carlo Michele, 2025. "A Novel Fuzzified Markov Chain Approach to Model Monthly River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1931-1951, March.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:4:d:10.1007_s11269-024-04053-x
    DOI: 10.1007/s11269-024-04053-x
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    References listed on IDEAS

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    1. Mahnoosh Moghaddasi & Shahab Araghinejad & Saeed Morid, 2013. "Water Management of Irrigation Dams Considering Climate Variation: Case Study of Zayandeh-rud Reservoir, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(6), pages 1651-1660, April.
    2. Farnood Freidooni & Hooshmand Ataei & Fatemeh Shahriar, 2015. "Estimating the Occurrence Probability of Heat Wave Periods Using the Markov Chain Model," Journal of Sustainable Development, Canadian Center of Science and Education, vol. 8(2), pages 1-26, February.
    3. Boulange, Julien & Watanabe, Hirozumi & Akai, Shinpei, 2017. "A Markov Chain Monte Carlo technique for parameter estimation and inference in pesticide fate and transport modeling," Ecological Modelling, Elsevier, vol. 360(C), pages 270-278.
    4. Hossein Tabari & Reza Zamani & Hossein Rahmati & Patrick Willems, 2015. "Markov Chains of Different Orders for Streamflow Drought Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3441-3457, July.
    5. Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    6. Mohammad H. Golmohammadi & Hamid R. Safavi & Samuel Sandoval-Solis & Mahmood Fooladi, 2021. "Improving Performance Criteria in the Water Resource Systems Based on Fuzzy Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 593-611, January.
    7. Hsin-Fu Yeh & Hsin-Li Hsu, 2019. "Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation," Sustainability, MDPI, vol. 11(6), pages 1-18, March.
    8. Wei Li & Xiaosheng Wang & Shujiang Pang & Haiying Guo, 2022. "A Runoff Prediction Model Based on Nonhomogeneous Markov Chain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1431-1442, March.
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