Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study

Hao Yang and Weide Li ()
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Hao Yang: Lanzhou University
Weide Li: Lanzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 1, No 28, 557-581

Abstract: Abstract Accurate and reliable runoff prediction is essential for water resources management. In this paper, a hybrid model STL-VMD-SFO-ESN which combines seasonal adjustment method (STL), variational mode decomposition (VMD), echo state network (ESN) and sailed fish optimizer (SFO) is proposed for daily runoff prediction. Daily runoff data from three different runoff monitoring stations in China’s Yellow River basin is used to evaluate the performance of proposed model and other newly reported models. The results indicates that: (1) The proposed model performs significantly better than the traditional data-driven models and some newly reported models. (2) STL decomposition can effectively remove the seasonal component of runoff and improve modeling accuracy. (3) ESN has a strong potential in runoff prediction, and its performance can be greatly improved by using bio-optimization algorithms. Thus, this new model has strong potential for runoff prediction for further application.

Keywords: Runoff prediction; Divide-and-conquer; Seasonal adjustment method; Echo state network; Variational mode decomposition; Sailed fish optimizer (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-022-03389-6

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