基于混沌小波与支持向量机的径流预测

以淮河流域沙颍河水系沙河和澧河上游的水库为例,建立了月均流量的混沌小波支持向量机组合预报模型,充分利用了混沌分析的相空间重构、小波分析的多分辨率功能以及支持向量机的非线性逼近能力,并采用NSE、PBIAS和RSR对组合预测模型进行了评价。结果表明:混沌小波支持向量机组合预测模型的识别期和验证期模拟精度均较高,均优于混沌支持向量机模型。     

应用光滑支持向量机预测汉江流域降水变化

统计学降尺度方法是国内外研究全球气候模型尺度降解的热点问题。研究和探讨了基于光滑支持向量机的统计学降尺度方法;建立大尺度气候观测资料和实测降水之间的统计关系;模拟和预测汉江流域降水变化,并同传统的多元线性回归分析方法相比较。结果表明,基于光滑支持向量机的统计学降尺度方法的模拟精度不仅高于多元线性回归分析方法,而且明显优于CGCM2气候模型的输出降水结果。     

信息量支持下SVM模型滑坡灾害易发性评价

三峡库区是我国滑坡灾害广泛发育的地区之一,滑坡灾害易发性评价对库区的防灾减灾有重要意义。在滑坡灾害易发性指标信息量的基础上,构建了信息量支持下的SVM模型,并对滑坡灾害易发性进行了评价。该模型根据地层岩性、地质构造、坡度、坡向、坡型结构、土地利用类型、水、归一化植被指数,以及上述指标的总信息量,共9类指标组成的数据集进行SVM训练,得到评价模型;运用该评价模型对研究区全区滑坡灾害易发性进行评价,并以模型决策值的零点和突变点确定易发性等级划分标准。并以三峡库区万州主城区为研究区验证模型,研究表明信息量支持下SVM模型的训练样本精度为81.41%,验证样本精度为91.11%,优于常用的信息量模型,滑坡的高易发区和较高易发区占研究区总面积的47.05%,主要集中在人类工程活动强烈的长江干支流两侧,结果与已知滑坡分布基本一致,表明该模型在研究区具备较好的适用性。     

改进支持向量机模型在城市需水量预测中的应用

以某城市需水量为研究对象,运用改进的支持向量基模型对该地区1991年到2001年的用水量进行模拟计算,并用该市2002年和2003年的用水量进行模型检验,与GM(1.1)模型所得的结果作比较,分析证明了改进的SVR模型方法能取得更好的结果。     

基于小波分解和支持向量机的大坝位移监控模型

常规大坝安全监控统计模型未能分别针对监测序列值内系统信号和随机信号特点进行模拟,故预报精度存在一定的提升空间。基于小波分解技术,利用监测序列值信号频率特征分离出系统信号与随机信号,并结合逐步回归与支持向量机(SVM)对不同信号的处理优势,在引入网格寻优与交叉验证确定SVM敏感参数的基础上,提出了一种基于多元统计结合小波分解和支持向量机的大坝位移监控模型,同时编制了其相应的计算程序。工程算例表明,该模型较常规模型能够同时考虑监测序列中的系统信号和随机信号,并且具有较强的模型寻优能力和更高的预报精度,从而验证了所建模型的有效性,该方法亦可推广应用于高边坡及大坝其他预警指标的监控。     

混凝土坝安全监控最小二乘支持向量机模型

传统的混凝土坝安全监控模型难以精确反映大坝变形的非线性变化规律,一定程度上影响模型的预测效果。基于统计学习理论的支持向量机,采用结构风险最小化准则,具有结构简单、理论完备、适应性强、全局优化、训练时间短、泛化性能好等优点。将最小二乘支持向量机应用于大坝安全监控领域,建立了混凝土坝的支持向量机监控模型。工程案例证明,该模型精度较高,具有广泛的实用性。     

人工免疫算法优化双支持向量机在拱坝变形预测中的应用

为了能够通过监测数据直观反映出坝体是否处于稳定运行状态,采用人工免疫算法优化的双支持向量机方法,对高拱坝变形数据进行了拟合预测分析,双支持向量机与标准支持向量机相比极大地提高了计算速度,在进行批量重复计算中计算效率明显提升。针对双支持向量机计算结果受参数影响较大且参数多的问题,引入人工免疫算法搜寻双支持向量机参数,人工免疫算法在遗传算法的基础上保留了一定数量的较优解,提升了算法的搜索效率。工程算例分析表明,参数对双支持向量机结果影响较大,通过人工免疫算法搜寻最优参数后,双支持向量机能够较好地拟合拱坝坝体变形数据,预测结果符合工程精度要求,最大误差仅为1 mm左右。     

Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.     

基于支持向量机的混凝土强度预测

支持向量机是建立在统计学理论之上的机器学习技术。提出了混凝土28 d抗压强度预测的一种新方法,即支持向量机回归方法。该方法根据有限的学习样本,建立了各种影响因素和混凝土抗压强度之间的一种非线性映射,可以对混凝土强度进行预测。以实际样本数据进行训练,并对测试样本进行了预测。预测结果表明,支持向量机方法有着良好的泛化能力,优于人工神经网络建模方法。     

大坝监控小波去噪变权支撑向量回归机模型

为有效利用监控模型和指标判别大坝安全性态,实现大坝安全预警。首先利用小波分析对实测数据进行去噪处理,在此基础上利用支撑向量机小样本建模和高泛化能力,考虑不同幅度测值对大坝预警所起的作用不同,从而建立大坝监控变权支撑向量回归机模型。工程实例和理论分析表明,模型具有一定的理论和实用价值。     

基于支持向量机的中长期入库径流预报

采用基于支持向量机的预测模型对水库中长期入库径流进行预报,建立径流预报的SVM模型。预报因子的优劣决定着预测精度的高低。为了提高预报精度,尝试采用模糊优选法对预报因子进行优选。将所建模型应用于新疆雅马渡站的径流预测中,并与没有进行预报因子优选的SVM模型进行比较。结果表明,进行预报因子优化后的SVM模型明显提高了径流的预报精度,具有更好的应用价值。     

An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System,Support Vector Machine and Artificial Neural Network Models

Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R²).     

Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model

Water Resources Management - To improve the accuracy of medium and long-term precipitation prediction, we propose an innovative application of the wavelet decomposition-prediction-reconstruction...     

支持向量机在大坝渗流监测中的应用

支持向量机是基于统计学习理论的小样本学习方法,是一种处理高度非线性分类回归等问题的新方法,它能较好地解决小样本非线性高维数,避免了神经网络无法解决的局部极小问题.本文简要介绍了支持向量机的基本原理及其在渗流监测数据处理中的应用,论述了如何利用支持向量机建立大坝渗流统计模型和预报.通过对云龙水库渗流监测连续观测数据的计算和分析,并与RBF神经网络预测结果进行比较,证明支持向量回归机在渗流监测中比RBF神经网络预测精度更高,具有良好的泛化能力.     

Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree

Estimation of scour downstream of a ski-jump bucket has been a topic of research among hydraulic engineers. For estimation of scour downstream of ski jump bucket, several empirical models are in use. In recent years, there has been emphasis to develop models which are capable of producing scour with high accuracy. Use of Artificial Neural Network (ANN) approach to model depth, width and length of scour hole indicates that performance of ANN models is far better than existing empirical models. At present, use of Support Vector Machines (SVMs) and M5 Pruned Model Tree are being considered in different disciplines to further improve upon the performance of ANN models as a potential alternate. With this in view, the present study deals with the development of regression models for computing various parameters of scour hole using SVMs and M5 Model Tree. A comparative evaluation of the performance of ANN versus SVMs and M5 Model Tree clearly shows that SVMs and M5 Model Tree can prove more useful than ANN models in estimation of scour downstream of a ski jump bucket. Further, M5 model tree offers explicit expressions for use by design engineers.     

基于支持向量机的复合地基承载力预测方法研究

提出一种基于支持向量机的复合地基承载力预测方法。该方法从复合地基试验结果中提取特征参数,组成反映复合地基竖向承载力的特征向量,并利用一种改进的支持向量机的非线性映射特性和学习能力,建立特征向量和复合地基承载力之间的非线性隐式方程,用以预测复合地基承载力。实例研究表明基于支持向量机的复合地基承载力预测方法预测结果较为准确,具有一定的实用价值。     

相空间重构支持向量机在径流模拟中的应用研究

将相空间重构理论引入月径流模拟中,利用C-C算法进行相空间重构,将一维径流时间序列拓展为多维,基于交叉验证支持向量机(CV-SVM)原理及方法,构建以相空间重构理论与支持向量机相结合的径流时间序列模拟模型,并构建传统BP、双隐层BP及GA-BP径流时间序列模拟模型作为对比模型,以盘龙河龙潭寨月径流时间序列为例进行分析。结果显示:基于相空间重构理论的CV-SVM模拟模型能较好地处理复杂的径流序列,在长达200个月的测试样本模拟中,平均相对误差e MRE、最大相对误差e MaxRE分别为0.571 7%,5.526 7%,决定系数DC和合格率QR分别为0.999 9和100%。表明该模型具有较高的泛化能力和模拟精度,模拟效果明显优于传统BP、双隐层BP模型,甚至优于GA-BP模型;表明研究建立的基于相空间重构理论的CV-SVM模型用于径流模拟是合理可行的,可为径流模拟提供方法和参考。     

Detecting Multi-Purpose Reservoir Operation Induced Time-Frequency Alteration Using Wavelet Transform

It is well recognized that natural flow variability is an inherent characteristic of rivers. Altered natural flow regime caused by anthropogenic regulations would threaten ecosystem biodiversity and deteriorate riverine health. Wavelet transform is a newly-developed tool that extracts dominant modes of variability by decomposing a non-stationary series into time-frequency space, which can be used to detect hydrologic alteration at various scales caused by reservoir operation. Continuous wavelet transform is simultaneously applied to recorded hourly inflow and outflow series of 1998–2008 for the Feitsui Reservoir located in northern Taiwan. Differences between wavelet power spectrum obtained for outflow and inflow series denote severity of hydrologic alteration. Greater spectral alteration is observed at less-than-1-day scales due to peak-load hydropower releases. The spectral alteration gradually declines with increasing scales. Different variation patterns for the yearly time-averaged spectral difference also reveal that the altered spectrum depends on hydrologic conditions. The index of spectral alteration (ISA), defined as the mean absolute deviations of power spectrum for all scales over a certain time period, is proposed to quantitatively assess severity of altered natural flow regime. ISA of 5 can be roughly recognized as the division of dry and non-dry years for the Feitsui Reservoir case. The obtained results offer decision makers useful information to adopt adaptive operating strategies to mitigate negative impacts of altered natural flow regime and derive optimal trade-off between human and environmental needs.     

基于小波变换的楚雄市近50年降水序列多时间尺度研究

以滇中高原核心城市楚雄市为研究对象,分30个代表性气象站点1950—2009年降水时间序列,通过Morlet小波变换等方法对其进行多时间尺度研究,探讨其演变趋势和变化规律。结果表明楚雄市降水序列均呈较明显的增加趋势,对于各种时间尺度的降水序列均表现出一致的降水变化规律,传统干旱区干湿季节差异性缩小;年降水相关性最好,最大6、24 h降水量相关性非常高,短历时(即最大1、6、24 h)降水与中长时间尺度(即月、年)降水相关性最差,短历时降水对中长时间尺度降水的影响较小,区域使用的数据资料序列小于由气象资料数据序列的长度限制,说明周期性的可靠度不高,还需要进一步根据更长的序列资料进行验证。