基于CEEMDAN-QPSO-BLS模型的径流预测研究

Runoff Prediction and Analysis Based on CEEMDAN-QPSO-BLS Method

准确的径流预测是水资源优化配置和高效利用的前提,是制定防洪减灾决策的基础,然而受到人类活动、环境、气候等因素的影响,径流序列呈现出非线性、非稳态、多尺度变化的特点,这为径流的精准预测增加了难度。为提高径流预测的精准度和可信度,结合自适应噪声完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)方法,量子粒子群优化算法(Quantum Particle Swarm Optimization,QPSO)、宽度学习系统(Broad Learning System,BLS)模型,提出了一种基于CEEMDAN-QPSO-BLS组合式的径流预测模型。该组合模型首先使用CEEMDAN方法对原始径流信号进行分解,得到若干相对平稳的本征模态分量。其次利用QPSO算法对BLS模型的特征层节点组数、增强层节点组数和组内节点数进行寻优,得到最优的宽度学习网络拓扑结构,进而使用最优的QPSOBLS对多个稳态分量进行预测,并对预测分量进行重构,从而获得更高的预测精度。以黄河流域小浪底水库的日径流值为实验数据,将EMD-QPSO-BLS、QPSO-BLS作为CEEMDAN-QPSO-BLS的对比模型,并采用纳什效率系数(NSE)、均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)作为模型预测可信度和精准度的评价指标。实验表明,在预见期4天内,与QPSO-BLS、EMD-QPSO-BLS模型相比,CEEMDAN-QPSO-BLS的预测精准度分别提高了79.87%、19.80%,可信度分别提高了131.2%、10.98%,径流预测精度的提高,可为防洪抗旱保护人民生命财产和可持续发展提供决策支持。

An accurate runoff prediction is the prerequisite for the optimal allocation and efficient utilization of water resources,and the basis for making flood control and disaster reduction decisions.However,due to the influence of human activities,environment,climate and other factors,runoff series show nonlinear,unsteady and multi-scale changes,which increases the difficulty of accurate runoff prediction.In or⁃der to improve the accuracy and credibility of runoff prediction,this paper combines the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)method.Quantum Particle Swarm Optimization(QPSO),Broad Learning System(BLS)model,a com⁃bined runoff prediction model based on CEEEDAN-QPSO-BLS is proposed.Firstly,CEEMDAN method is used to decompose the original runoff signal to obtain several relatively stationary intrinsic mode components.Secondly,the QPSO algorithm is used to optimize the number of node groups in the feature layer,the number of node groups in the enhancement layer and the number of nodes in the group of BLS model,and the optimal topology structure of the width learning network is obtained.Then,the optimal QPSO-BLS is used to predict multiple steadystate components,and the prediction components are reconstructed so as to obtain higher prediction accuracy.In this model,the daily runoff value of Xiaolangdi Reservoir in the Yellow River Basin is used as the experimental data,and EMD-QPSO-BLS and QPSO-BLS are used as the comparison model of CEEMDAN-QPSO-BLS.Nash-Sutcliffe efficiency coefficient(NSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE)and Mean Absolute Percentage Error(MAPE)are used to evaluate the reliability and accuracy of the model predic⁃tion.The experimental results show that,compared with QPSO-BLS with EMD-QPSO-BLS models,the prediction accuracy of CEEMDANQPSO-BLS is improved by 79.87%and 19.80%,and the credibility is improved by 131.2%and 10.98%,respectively.This paper provides decision-making support for flood control and drought relief to protect people’s lives and property and sustainable development.