A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling

Fuping Liu, Ying Liu, Chen Yang () and Ruixun Lai
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Fuping Liu: North China University of Water Resources and Electric Power
Ying Liu: North China University of Water Resources and Electric Power
Chen Yang: North China University of Water Resources and Electric Power
Ruixun Lai: Yellow River Institute of Hydraulic Research

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 12, No 18, 4785-4797

Abstract: Abstract Precipitation is the most basic part of the water cycle process. Aiming at the problem of low prediction accuracy caused by the nonlinear and unstable characteristics of the precipitation series, a new precipitation prediction method based on the CEEMDAN-IWOA-BP coupling model is proposed. This method first uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original precipitation sequence, and obtains a series of intrinsic mode function (IMF) and residual terms (Res) as inherent potential influencing factors, innovatively introduce TENT chaotic mapping and roulette algorithm to improve the Whale Optimization Algorithm (WOA), use IMFs and Res as the input of the Improve Whale Optimization Algorithm (IWOA) to optimize Back Propagation (BP) neural network prediction model, and finally superimpose the predicted values as ultima result.The present method was applied to predict the annual precipitation from 1958 to 2017 in Sichuan Province. Compared with the prediction results of other models, the CEEMDAN-IWOA-BP coupled model has significantly improved prediction accuracy than the single model, and the prediction error index is smaller than the BP neural network optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms, moreover, the optimization accuracy and solving ability are significantly enhanced compared with the unimproved WOA. It can extract the information of complex precipitation series more effectively, and then provide a new method for nonlinear and unstable precipitation time series prediction.

Keywords: Precipitation prediction; Algorithm optimization; Complete ensemble empirical mode decomposition with adaptive noise; Improve whale optimization algorithm; BP neural network (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03277-z

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