短期负荷预测的Ensemble混沌预测方法

负荷记录中的噪声以及预测方法中矩阵数值计算的奇异性，使得一次预测得到的结果具有较大的误差。为了降低初值中噪声的不利影响，将数值天气预报中的Ensemble方法移植到短期负荷预测中。在混沌相空间重构预测中，在参考矢量上叠加一定强度的正态分布噪声，形成多个扰动后的参考矢量，分别预测后得到多个预测结果，再由这些预测结果合成概率化的预测结果。采用这种Ensemble技术，不仅可以提高预测准确率，还可以得到概率化的预测结果。     

基于多分辨率SVM回归估计的短期负荷预测

针对短期负荷预测支持向量机（SVM）方法的局部逼近能力和泛化能力进行研究，将多分辨率支持向量机（M-SVM）用于短期负荷预测中节点负荷预测曲线的回归估计。该理论在保持曲线总体逼近能力的同时提高了局部区域的逼近能力。文中根据短期负荷预测的具体特点，设计了负荷预测数学模型，采用96条回归曲线进行日负荷的曲线预测，并在该模型的基础上采用实际数据进行验证，分析了这种回归模型的泛化能力。实验结果表明M-SVM模型在预测精度和预测速度方面具有优良的特性。     

基于预测误差分布特性统计分析的概率性短期负荷预测

现有短期负荷预测方法一般只能给出确定性负荷预测结果，难以满足电力市场中不确定性风险分析决策的要求。文中提出了一种基于负荷预测误差特性的统计分析的概率性预测方法。该方法首先从时段与负荷水平2个联合维度上建立了对预测误差分布规律进行统计分析的模型，并提出了检验该统计规律有效性的原则和方法;将验证后的预测误差统计分布规律与确定性的负荷预测结果相结合，即可得到概率性的负荷预测结果。基于该结果，还能求取某一置信水平下的预测负荷曲线的包络线。结合实际电网数据验证了所提出方法的有效性和实用性，为概率性短期负荷预测提供了一条可行的新思路。     

基于短中期水文预报的三峡水库优化调度运用

准确、及时的水文预报是水库优化调度的基础和技术支撑,是科学化、精细化调度的保障。目前三峡水库预报范围已覆盖长江上游流域78万km2,一周以内的短中期水文预报成果准确、可靠。总结了试验蓄水期以来三峡水库基于短中期水文预报的各种优化调度实践,可为其他水库调度提供借鉴。     

广东韩江年最大流量的预测研究

海洋、大气、地表径流是地球圈内水汽循环的一个整体。把北半球高空等压面高度场和太平洋海温场当作影响广东长期港水变化的重要因子，是符合气候学和水文学原理的。通过主分量的分析方法，建立北半球500hPa高度场、北太平洋海温场与广东韩江年最大流量长期变化的统计关系，从而作出翌年的年最大流量预测，取得了比较满意的成果。     

改进的投影寻踪技术在实际预报中的应用

针对传统的投影寻踪技术中投影与寻踪两个核心问题进行改进,用实数编码的遗传算法代替高斯一牛顿进行投影方向优化、利用非线性参数Hermite多项式代替非参数方法估计岭函数。建立基于改进的投影寻踪预测技术水电站入库流量预报模型,并将模型用于杂谷脑流域水电站的入库流量预测,研究表明改进的投影寻踪技术能够以较快速度收敛于模型全局最优解,研究成果为杂谷脑梯级水电站中期调度奠定了基础,也为其他流域水电站中期入库流量预报提供了重要借鉴。     

珠江流域中长期水文预报现行主要方法浅析

中长期水文预报是一门边缘学科。目前应用于珠江流域中长期水文预报的主要方法有4种，即历史演变法、周期均值迭加法、转移概率法、数理统计相关分析法。对珠江流域近几年中长期水文预报所有方法的基本原理进行总结，并结合实际对各种方法作进一步的分析。     

石羊河流域中长期径流预报模型应用

以石羊河流域西营水库为研究对象,采用时间序列模型(逐步回归自回归组合模型、ARMA模型)和改进的人工神经网络模型(逐步回归BP神经网络模型、逐步回归RBF神经网络模型)进行中长期径流预报并对比分析,为石羊河流域水量调度系统设计提供参考。结果表明:4种预报方法都达到预报精度要求,其中RBF神经网络方法合格率最高,但耗时长,逐步回归自回归预报精度和模型耗时都比较合理,可为石羊河流域水资源调度提供参考。     

Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting

A new hybrid model, the wavelet–bootstrap–multiple linear regression (WBMLR) is proposed to explore the potential of wavelet analysis and bootstrap resampling techniques for daily discharge forecasting. The performance of the developed WBMLR model is also compared with five more models: multiple linear regression (MLR), artificial neural network (ANN), wavelet-based MLR (WMLR), wavelet-based ANN (WANN) and wavelet–bootstrap–ANN (WBANN) models. Seven years of discharge data from seven gauging stations in the middle reaches of Mahanadi river basin in India are applied in this study. Significant input vectors are decomposed into discrete wavelet components (DWCs) using discrete wavelet transformation (DWT) to generate wavelet sub time series that are used as inputs to the MLR and ANN models to develop the WMLR and WANN models, respectively. Effective wavelets are selected by considering several types of wavelets with different vanishing moments. WBMLR and WBANN models are developed as ensemble of different WMLR and WANN models, respectively, developed using different realizations of the training dataset generated using bootstrap resampling technique. The results show that the wavelet bootstrap hybrid models (i.e. WBMLR and WBANN) produce significantly better results than the traditional MLR and ANN models. Hybrid models based on MLR (WMLR, WBMLR) perform better than the ANN based hybrid models (WBANN, WANN). The WBMLR and WMLR models simulate the peak discharges better than the WBANN, WANN, MLR and ANN models, whereas the overall performance of WBMLR model is found to be more accurate and reliable than the remaining five models.     

Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

One of the key elements in achieving sustainable water resources and environmental management is forecasting the future condition of the surface water resources. In this study, the performance of a river flow forecasting model is improved when different input combinations and signal processing techniques are applied on multi‐layer backpropagation neural networks. Haar, Coiflet and Daubechies wavelet analysis are coupled with backpropagation neural networks model to develop hybrid wavelet neural networks models. Different models with different input selections and structures are developed for daily, weekly and monthly river flow forecasting in Ellen Brook River, Western Australia. Comparison of the performance of the hybrid approach with that of the original neural networks indicates that the hybrid models produce significantly better results. The improvement is more substantial for peak values and longer‐term forecasting, in which the Nash–Sutcliffe coefficient of efficiency for monthly river flow forecasting is improved from 0.63 to 0.89 in this study. Copyright © 2014 John Wiley & Sons, Ltd.     

Performance Assessment of the Linear,Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.     

A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models

Accurate forecast of the magnitude and timing of the flood peak river discharge and the extent of inundated areas during major storm events are a vital component of early warning systems around the world that are responsible for saving countless lives every year. This study assesses the forecast accuracy of two different linear and non-linear approaches to predict the daily river discharge. A new linear stochastic method is produced by evaluating a detailed comparison between three pre-processing approaches, differencing, standardization, spectral analysis, and trend removal. Daily river discharge values of the Bow River with strong seasonal and non-seasonal correlations located in Alberta, Canada were utilized in this study. The stochastic term for this daily flow time series is calculated with an auto-regressive integrated moving average. We found that seasonal differencing is the best stationarization method for periodic effect elimination. Moreover, the proposed non-linear Group Method of Data Handling (GMDH) model could overcome the known accuracy limitations of the classical GMDH models that use only two inputs in each neuron from the adjacent layer. The proposed new non-linear GMDH-based method (named GS-GMDH) can improve the structure of the classical linear GMDH. The GS-GMDH model produced the most accurate forecasts in the Bow River case study with statistical indices such as the coefficient of determination and Nash-Sutcliffe for the daily discharge time series higher than 97% and relative error less than 6%. Finally, an explicit equation for estimation of the daily discharge of the Bow River is developed using the proposed GS-GMDH model to showcase the practical application of the new method in flood forecasting and management.

     

Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Water Resources Management - Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid...     

Uncertainty Assessment of the Integrated Hybrid Data Processing Techniques for Short to Long Term Drought Forecasting in Different Climate Regions

Water Resources Management - Accurate prediction of drought indices is a useful method to reduce its undesirable consequences. In this study, the workability of newly integrated hybrid forecasting...     

船舶横摇运动的非线性振动与混沌

本文针对船舶非线性横摇运动模型，以波浪尺度为变参数，运用平均化方法和范德坡变换，一系统的振动解随参数变化的定性情况，然后通过数值积分和胞映射相结合的方法，确定系统的多种开熙熙攘攘夺动解。可以看到胞映地能灵活地处理各种不同形式的上子，如周期解，各阶亚谐解乃至混沌吸引子并能方便快速地求解。横摇运动的大量非线性现象，如吸引子工存，对称性碳缺，倍周期分岔等现象都被观察到，文中还给出了由一系列倍周期分岔导致     

Stage and Discharge Forecasting by SVM and ANN Techniques

In this study, forecasting of stage and discharge was done in a time-series framework across three time horizons using three models: (i) persistence model, (ii) feed-forward neural network (FFNN) model, and (iii) support vector machine (SVM) model. For these models, lagged values of the time series constituted the set of input variables which had been selected by principal component analysis (PCA). Parameters of FFNN and SVM models were determined by sensitivity analysis. All the three models were evaluated using data from Mahanadi River, India, and their forecasting performance was then compared. It is shown that over a shorter forecasting horizon, it is difficult to outperform the persistence model. Moreover, results show that forecasting of stage and discharge over a longer time frame by the SVM model is more accurate than that by the other two models.     

西北江三角洲来水来沙的非线性分形特征

根据西、北江干流高要、石角及西北江三角洲网河区顶点马口、三水四个水文站的流量和含沙量资料 ,应用非线性分形原理的R S方法 ,计算西北江干流及西北江网河区顶点四个水文站的流量、含沙量的赫斯特数H和分形维数D ;探讨西、北江干流来水来沙的分形特征及西北江三角洲的分水分沙规律。结果表明 ,流量的分维数略大于含沙量的分维数 ,三水站流量及含沙量的分维数与其上游的石角站流量、含沙量的分维数相差较大 ,与思贤的分水分沙造成西北江的水沙交换有关     

中长期径流预报研究进展及发展趋势

从中长期径流演变规律、径流预报因子识别、径流预报模型和径流预报系统4个方面阐述了中长期径流预报的研究进展,在此基础上分析了中长期径流预报面临的主要问题和发展趋势。未来中长期径流预报研究应更加注重多学科综合和跨学科交叉,更加注重预报方法的适用性及预报结果的可靠性、稳定性,更加注重高新技术的应用。     

洪山泉域水均衡分析及泉流量预测

基于洪山泉流量日益衰减的现状,以该泉域2001—2013年的泉流量、开采量等系列资料为基础,分析了泉域水均衡状况,得出其流量衰减的主要原因为不断增加的岩溶水开采和煤矿矿井排水。利用灰色综合关联度方法,对不同时滞影响下的降水量与还原泉流量进行灰色关联分析,确定降水量对还原泉流量影响的滞后时间为8 a。将多元回归模型计算的有效降水量作为还原泉流量的相关序列,建立了灰色GM(1,2)预测模型,并进行了残差修正。利用修正后的模型,对2014—2017年不同降水保证率下的还原泉流量进行了预测。结果表明:2015年当降水保证率为95%时,洪山泉将发生断流。     

中长期水文预报的预报区间估计方法

中长期水文预报由于影响因素复杂和目前科学水平的限制,还处于探索、发展阶段,预报手段仍以成因分析(物理因子相关)和数理统计方法为主。其中数理统计方法(方差分析、AR(P)模)是我们经常使用的一种重要方法。在使用数理统计方法进行中长期水文预报时,从理论上来说,给定一个预报区间比给定一个具体预报值更为合理。文章就数理统计法进行中长期水文预报如何给定估计合理预报区间进行初步探讨。