A Runoff Prediction Model Based on Nonhomogeneous Markov Chain

Wei Li, Xiaosheng Wang (), Shujiang Pang and Haiying Guo
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Wei Li: Hebei University of Engineering
Xiaosheng Wang: Hebei University of Engineering
Shujiang Pang: Hebei University of Engineering
Haiying Guo: Hebei University of Engineering

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 4, No 17, 1442 pages

Abstract: Abstract Runoff prediction is one of the important research fields of hydrology. As for the runoff series with unstable, poor periodicity and non-obvious tendency, this paper solves the problem that the general traditional models are not suitable for the short and medium-term prediction of such runoff series. To describe the nonhomogeneous characteristics of runoff series, a novel prediction model is established based on a nonhomogeneous Markov chain (NHMC-RPM). In this model, the probability distribution function of weekly runoff is obtained and the predicted value is calculated using the expected state. Taking the Yellow River as a case, the prediction results show that the NHMC-RPM is more accurate than other traditional models. The model presented in this work may be used to deal with similar runoff or other series data, as well as provide a practical approach for river managers to predict short and medium-term runoff.

Keywords: Nonhomogeneous Markov chain; Runoff prediction; Probability distribution; Weekly runoff series (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03091-7

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