Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow

Annual streamflow prediction is of great significance to the sustainable utilization of water resources, and predicting it accurately is challenging due to changes in streamflow have strong nonlinearity and uncertainty. To improve the prediction accuracy of annual streamflow, this study proposes a new hybrid prediction model based on extracting information from high-frequency components of streamflow. In the proposed model, the original streamflow data is decomposed by ensemble empirical mode decomposition (EEMD) into several intrinsic mode functions (IMFs) with different frequencies. Then, the dominant component and residual component are identified from the high-frequency components IMF1 and IMF2 using singular spectrum analysis (SSA), and the residual components are accumulated as a new component. Finally, all the components, including the new component that is not noise, are modelled by support vector machine (SVM), and the SVM is optimized by grey wolf optimizer (GWO). To analyse and verify the proposed model, the annual streamflow data are collected from the Liyuan River and Taolai River in the Heihe River Basin, and six models, autoregressive integrated moving average (ARIMA), cross validation (CV)-SVM, GWO-SVM, EEMD-ARIMA, EEMD-GWO-SVM and modified EEMD-GWO-SVM are considered as comparison models. The results indicate that the prediction performance of the proposed model is obviously better than that of other reference models, and extracting valuable information from high-frequency components can effectively improve annual streamflow prediction. Thus, the high-frequency components contained in the original streamflow series have an important impact on obtaining accurate streamflow prediction, and the proposed model makes full use of the high-frequency components and provides a reliable method for streamflow prediction.

     

基于参数优化的EEMD-LSSVM年径流组合预测模型

径流预测是水资源管理的基础,其准确性直接影响水资源优化调度的成果。本文针对径流时间序列的内在周期特性,引入一种基于总体经验模态分解（EEMD）的LSSVM组合预测模型,并提出一种基于动态逼近局部搜索粒子群的LSSVM参数寻优方法。基于分解一重构原则,论文首先利用总体经验模式分解法对径流系列进行周期分量提取,然后应用基于参数寻优的LSSVM模型对各分量进行预测和重构。以澧水流域江垭站的年径流预测为例进行模型检验,通过三种预测模型的结果对比,验证了本文组合预测模型的可靠性。     

Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks

Accurate and reliable runoff forecasting plays an increasingly important role in the optimal management of water resources. To improve the prediction accuracy, a hybrid model based on variational mode decomposition (VMD) and deep neural networks (DNN), referred to as VMD-DNN, is proposed to perform daily runoff forecasting. First, VMD is applied to decompose the original runoff series into multiple intrinsic mode functions (IMFs), each with a relatively local frequency range. Second, predicted models of decomposed IMFs are established by learning the deep feature values of the DNN. Finally, the ensemble forecasting result is formulated by summing the prediction sub-results of the modelled IMFs. The proposed model is demonstrated using daily runoff series data from the Zhangjiashan Hydrological Station in Jing River, China. To fully illustrate the feasibility and superiority of this approach, the VMD-DNN hybrid model was compared with EMD-DNN, EEMD-DNN, and multi-scale feature extraction -based VMD-DNN, EMD-DNN and EEMD-DNN. The results reveal that the proposed hybrid VMD-DNN model produces the best performance based on the Nash-Sutcliffe efficiency (NSE?=?0.95), root mean square error (RMSE?=?9.92) and mean absolute error (MAE?=?3.82) values. Thus the proposed hybrid VMD-DNN model is a promising new method for daily runoff forecasting.

     

年径流预测的Shepard插值模型

年径流现象是多种因子综合作用的结果,各预测因子与年径流之间是复杂的非线性关系。目前提出的年径流预测模型大多用显式函数来表示,其具体的函数形式需随研究地区的不同而作相应的改变,求解这些函数一般较复杂。实际预测工作则常常是把本次年径流的预测因子值与当地年径流预测因子历史样本系列逐个进行比较分析,实践表明这种方法行之有效。为此,提出用Shepard插值方法构建年径流预测的新模型（SP模型）。实例研究的结果说明,SP模型简便、实用性强,可在径流中长期预测中广泛应用。     

基于自适应变分模态分解和长短期记忆网络的月径流预报

准确可靠的月径流预报是流域水旱灾害防治及水资源合理配置的重要依据。原始径流时间序列包含多种频率成分，将时间序列数据分解预处理技术和机器学习模型相结合的混合模型已被用于捕捉径流动态过程。然而，将数据分解技术直接应用于整个时间序列是一种不切实际的方法，会导致部分信息从测试阶段传输到模型的训练过程中。为此，设计了一个用观测数据更新历史样本的自适应动态分解策略，提出基于自适应变分模态分解和长短期记忆网络的分解-预测-集成月径流预测混合模型。首先，采用自适应分解策略对径流时序数据进行变分模态分解，得到不同频率成分的子序列；其次，为每个分解子序列构建长短期记忆神经网络径流预测模型，并采用贝叶斯优化算法优选模型超参数；然后，将子序列的预测结果集成得到径流的最终预测结果；最后，以金沙江上游石鼓水文站月径流预报为研究实例，对比传统的分解策略(“捆绑分解”)和分解方法(离散小波变换和集成经验模态分解),验证所提混合模型的有效性和可行性。结果表明，所提混合模型在数据分解预处理中避免了引入未来信息，并能够进一步提升径流预报精度。     

A Novel Hybrid Method for River Discharge Prediction

Accurate prediction of river discharge is essential for the planning and management of water resources. This study proposes a novel hybrid method named HD-SKA by integrating two decomposition techniques (termed as HD) with support vector regression (SVR), K-nearest neighbor (KNN) and ARIMA models (combined as SKA) respectively. Firstly, the proposed method utilizes local mean decomposition (LMD) to decompose the original river discharge series into sub-series. Next, ensemble empirical mode decomposition (EEMD) is employed to further decompose the LMD-based sub-series into intrinsic mode functions. Further, the EEMD decomposed components are used as inputs in three data-driven models to predict river discharge respectively. The prediction of all components is then aggregated to obtain the results of HD-SVR, HD-KNN and HD-ARIMA models. The final prediction is obtained by taking the average prediction of these models. The proposed method is illustrated using five rivers in Indus Basin System. In five case studies, six models were built to compare the performance of the proposed HD-SKA model. The data analysis results show that the HD-SKA model performs better than all other considered models. The Diebold-Mariano test confirms the superiority of the proposed HD-SKA model over ARIMA, SVR, KNN, EEMD-ARIMA, EEMD-KNN, and EEMD-SVR models.

     

泾河张家山站水沙多时间尺度分析及输沙量模拟

为了掌握泾河水沙变化的基本规律,运用EMD方法对泾河张家山水文站1958—2011年的年径流量及年输沙量序列分别进行了多时间尺度分解,依据实测年输沙量数据,应用时间序列分析方法建立了年输沙量模拟模型。泾河年径流和年输沙量的时间序列均可分解为3个不同波动周期的振荡分量和一个递减的趋势分量;年输沙量模型适用性较好,且模拟精度较高,可应用于年输沙量预测。泾河水沙多时间尺度变化的特征分析和输沙量预测可为泾河水资源规划提供科学依据。     

小波神经网络在径流预测中的应用

将小波分析与传统的BP神经网络模型进行组合,提出了一种新的径流中长期预测方法。该方法对年径流序列进行Mallat小波分解,将分解后得到的不同尺度下的低频成分和高频成分分别进行Mallat算法重构,对重构系列采用BP神经网络模型进行预测。采用黄河三门峡站1470-2002年的年径流资料进行模型的预测和检验,并与传统的BP神经网络模型进行比较,研究结果表明小波神经网络在径流预测中具有较好的预报精度,可以成功地用于径流模拟和预测。     

Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model

Water Resources Management - Improving the accuracy of hydrological prediction for long-term annual runoff series is important for water resources management and planning. In this study, the...     

黄河流域典型流域水文气象变化与径流过程模拟

典型流域水文气象分析及水文过程模拟是合理评价黄河流域水资源变化的重要基础。利用Mann-Kendall秩次相关检验法和均值差异T检验法分析了黄河流域7个典型流域的年降水量、年径流量和年均气温序列的变化特征,并采用VIC模型、新安江模型、WBM模型和GR4J模型模拟站点的径流过程。结果表明:各典型流域年均气温显著升高;年降水量变化趋势和突变情况均不明显;而年径流量呈显著下降趋势,且突变多发生在1980s。模型模拟结果表明4种水文模型在黄河流域应用效果较好。就Nash效率系数而言,VIC和新安江模型因为考虑产汇流过程较为详细,模拟径流过程与实测过程更贴近;就径流相对误差而言,WBM模型在水量模拟计算中表现较优,其结构简单,适用于流域水资源的模拟与评价。     

黄河下游年径流量及输沙量变化的EMD分析

运用EMD方法对黄河下游花园口站1952—2009年年径流量及年输沙量的时间序列资料分别进行了多时间尺度分析,得到了各自具有不同周期的波动分量以及趋势分量。结果表明:黄河下游年径流量、年输沙量的变化过程具有复杂的多时间尺度特性,是多种波动成分共同作用的结果;年径流量和年输沙量变化过程均存在3个波动周期,而趋势项呈递减的态势;相同尺度下的水、沙分量之间具有显著的相关性,而不同尺度下的水、沙分量之间基本无相关性。     

R/S-GM(1,1)组合模型在径流预测中的应用

基于R/S分析法能提供有效的非线性科学预测,河川径流具有灰色禀性,为了提高河流径流预报精度,提出了R/S分析与灰色理论相结合的河川径流预测方法。该方法可以克服径流灰色预测存在的数据波动较大时预测精度降低的缺陷。将该方法应用到黑河莺落峡站和正义峡站的年径流量、汛期和非汛期径流量6个序列进行径流预测验证。结果表明:两站年径流量和汛期径流量序列的预测精度都在90%左右,非汛期径流量序列在80%以上,各径流序列预测结果与Mann-Kendall趋势检验一致,预测结果可靠,为河流径流量的科学预测提供了一种新方法。     

不同组合小波神经网络模型对径流预测的适用性

针对水文时间序列的非平稳性特征,以长江三峡宜昌站1904~2003年年平均流量为例,分别建立了小波分析（WA）与BP神经网络和径向基函数神经网络（RBF）耦合的预测模型,探究了两种组合模型的预测效果,并与传统的单一人工神经网络模型对比;并采用5种常见的预测性能评价指标分析预测效果。结果表明：组合模型预测成果的精度较单一模型显著提高;组合和单一模型中RBF网络模型均优于BP网络模型;小波径向基函数神经网络组合模型具有较优的预测精度和泛化能力,是提高预测精度的有效方法,在径流预测中具有可行性。     

基于EMD的密云水库年径流量多时间尺度分析

针对小波变换的不足之处,运用EMD方法对密云水库1956—2000年还原后的入库天然年径流量时序进行多时间尺度分析,发现其变化存在准2～4a、准5～6a、准8～9a、准11a和准17～19a的波动周期,并分析了各分量的趋势变化,对密云水库运行管理工作的科学化具有重要借鉴意义。     

基于Matlab神经网络的流域年径流量预测

阐述了运用人工神经网络模型对流域年径流量径流序列做出预报,表明人工神经网络模型在水文预报中具有一定的优势。通过BP神经网络算法得到了适合该神经网络模型的训练算法。以渔峡口站年径流量实测序列为研究对象,在数值试验的基础上建立了年径流序列预报的人工神经网络预报模型结构,提高了该模型的预报准确性。     

基于EEMD-AR模型的丹江口水库年径流随机模拟与预报

基于水库历史年入库径流序列组分分析和识别,采用线性趋势回归检验法、有序聚类法、方差线谱法等方法,推求出序列趋势项、跳跃项及周期项等确定性成分,提出基于集合经验模态分解法(EEMD方法)的水库年径流自回归随机模拟模型(EEMD-AR),并应用于丹江口水库的年径流随机模拟和预报中。通过EEMD分解,解决了当丹江口水库历史年径流序列为非平稳序列时不能直接应用自回归模型(AR)进行随机模拟和预报的问题。模拟结果表明,EEMD-AR模型能较好地模拟丹江口水库年径流序列并保持原历史序列的统计特性,且模型预报精度符合要求。     

Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting

High accuracy forecasting of medium and long-term hydrological runoff is beneficial to reservoir operation and management. A hybrid model is proposed for medium and long-term hydrological forecasting in this paper. The hybrid model consists of two methods, Singular Spectrum Analysis (SSA) and Auto Regressive Integrated Moving Average (ARIMA). In this model, the time series of annual runoff are first decomposed into several sub-series corresponding to some tendentious and periodic motions by using SSA and then each sub-series is predicted, respectively, through an appropriate ARIMA model, and lastly a correction procedure is conducted for the sum of the prediction results to ensure the superposed residual to be a pure random series. The annual runoff data of two reservoirs in China are analyzed as case studies. The results have been compared with the predictions made by ARIMA and Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF). It is shown that hybrid model has the best performance.     

淮河中游未控区间天然径流评价

淮河干流中游未控区间的天然径流是历次水资源评价的难点。在对1980~2000年天然径流系列进行逐项还原的基础上,将其计算结果与第一次评价的1956~1979年成果进行对比分析;对同步期1956~2000年的径流成果进行简单介绍。经与同步期多年平均天然径流深等值线图对照,反映本次同步期天然径流评价成果基本合理,可作为水资源评价的依据。     

汾河流域径流减少因素的定量解析

分析汾河流域 1981—2015 年气候、植被和人类取用水的演变特征。结果表明,流域年降水和净辐射分别呈 不显著（p>0.05）增加和减少趋势,叶面积指数显著（p<0.05）增加,人为取用水量波动增加,实测年径流显著（p<0.05） 减少。基于分布式水文模型和因素去趋势方法定量解析降水、净辐射、温度、叶面积指数以及取用水对汾河年径 流减少的贡献。结果表明,取用水的增加是导致汾河径流减少的主要原因,其次为温度、叶面积指数和净辐射的 变化。在上述 4 个因素无趋势情景下,实测年平均径流将分别增加 27.4%、14.4%,13.7% 和 2.1%。降水增加缓解 了年径流的减少。在年降水无趋势的情景下,实测年平均径流将减少 5.3%。上述研究结果可为汾河流域的水资 源管理和规划提供参考。     

基于GA-Elman神经网络模型的年径流预测

针对传统神经网络模型静态性及训练算法易陷入局部极值的缺陷,为了实现神经网络训练全局寻优,提高模拟精度,并使网络结构能动态反映年径流系列的时变特性,本文以年降雨及气温作为输入因子、年径流量为模型预测对象,结合遗传算法和Elman神经网络各自的优点,采用遗传算法对网络权值阈值全局优化,通过二者的耦合构建了GA-Elman年径流预测模型。利用构皮滩站1961—2015年的径流系列对模型进行了训练及测试,并对各模型预测性能比较分析。结果表明:GA-Elman模型预测平均相对误差5.29%、均方根误差55.81 mm,效果良好,对于径流预测具有实用价值;神经网络模型预测精度优于基于线性方法的模型,预测平均相对误差从12.01%降至7.07%以下;采用遗传算法改进神经网络权值阈值优化过程,预测平均相对误差从7.07%降低到5.29%,可明显提高模型泛化能力,从而改善径流预测效果。