Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling

In the present study, the Group method of data handling (GMDH) network was utilized to predict the scour depth below pipelines. GMDH network was developed using back propagation. Input parameters that were considered as effective parameters on the scour depth included those of sediment size, geometry of pipeline, and approaching flow characteristics. Training and testing performances of the GMDH networks have been carried out using nondimensional data sets that were collected from the literature. These data sets are related to the two main situations of pipelines scour experiments namely clear-water and live-bed conditions. The testing results of performances were compared with the support vector machines (SVM) and existing empirical equations. The GMDH network indicated that using of back propagation produced lower error of scour depth prediction than those obtained using the SVM and empirical equations. Also, the effects of many input parameters on the scour depth have been investigated.     

Support vector regression based shear strength modelling of deep beams

Support vector regression based modelling approach was used to predict the shear strength of reinforced and prestressed concrete deep beams. To compare its performance, a back-propagation neural network and the three empirical relations was used with reinforced concrete deep beams. For prestressed deep beams, one empirical relation was used. Results suggest an improved performance by the SVR in terms of prediction capabilities in comparison to the empirical relations and back propagation neural network. Parametric studies with SVR suggest the importance of concrete cylinder strength and ratio of shear span to effective depth of beam on strength prediction of deep beams.     

Prediction of local scour around bridge piers using the ANFIS method

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles’ initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville’s formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

     

基于神经网络的软件模块风险性预测模型

采用学习矢量量化神经网络对软件质量进行预测,提出基于学习矢量量化神经网络的软件模块风险性预测模型,与BP神经网络预测模型相比,实验结果表明提出的模型获得更精确的预测效果。     

针对设备端口链路的LSTM网络流量预测与链路拥塞方案

针对设备端口链路流量,提出两种基于长短期记忆网络的预测模型。第一种针对在大时间粒度下平稳变化的流量;第二种则针对在小时间粒度下波动剧烈的非平稳流量。通过选用不同的数据划分方式与模型训练方法,构建两种具有不同网络结构的流量预测模型。实验结果表明,前者在处理平稳变化的流量时能够达到极高的预测精度,后者在处理非平稳流量时具有明显优于SVR模型、BP神经网络模型的预测效果。在第二种预测模型的基础上,提出了参数可调的链路拥塞预警方案,实验证明该方案具有一定的可行性。     

Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes

This paper evaluates the potential of five modeling approaches, namely M5 model tree, random forest, artificial neural networks, support vector machines and Gaussian processes, for the prediction of unconfined compressive strength of stabilized pond ashes with lime and lime sludge. The study not only presents five models for the same set of data but also compares the overall performance of them. Dataset used consists of 255 samples acquired from laboratory experiments. Out of the total, 170 randomly chosen samples were used for training and remaining 85 were used for testing the models. Input dataset consists of eight parameters (uniformity coefficient, coefficient of curvature, maximum dry density, optimum moisture content, lime, lime sludge, curing period and 7-day soaked California bearing ratio), while the output is UCS value at 7, 28, 45, 90 and 180 days of curing. Comparisons of results propose that Gaussian processes modeling strategy works well and the overall performance was substantially nearer to the exact agreement line. As a result of GP model, higher value of CC = 0.997 and lower values of RMSE = 23.016 kPa and MAE = 16.455 were obtained for testing the dataset. Sensitivity analysis suggests that lime, lime sludge, curing period and California bearing ratio are the significant parameters for predicting the unconfined compressive strength of stabilized pond ashes. The results confirmed that GP models are in a position to predict the unconfined compressive strength of stabilized pond ashes with an excessive degree of accuracy; however, GP modeling approach proves that this approach is more economical and less difficult in comparison with tedious laboratory work.

     

Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways

The scour below spillways can endanger the stability of the dams. Hence, determining the scour depth downstream of spillways is of vital importance. Recently, soft computing models and, in particular, artificial neural networks (ANNs) have been used for scour depth prediction. However, ANNs are not as comprehensible and easy to use as empirical formulas for the estimation of scour depth. Therefore, in this study, two decision-tree methods based on model trees and classification and regression trees were employed for the prediction of scour depth downstream of free overfall spillways. The advantage of model trees and classification and regression trees compared to ANNs is that these models are able to provide practical prediction equations. A comparison between the results obtained in the present study and those obtained using empirical formulas is made. The statistical measures indicate that the proposed soft computing approaches outperform empirical formulas. Results of the present study indicated that model trees were more accurate than classification and regression trees for the estimation of scour depth.     

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

The process of local scour around bridge piers is fundamentally complex due to the three-dimensional flow patterns interacting with bed materials. For geotechnical and economical reasons, multiple pile bridge piers have become more and more popular in bridge design. Although many studies have been carried out to develop relationships for the maximum scour depth at pile groups under clear-water scour condition, existing methods do not always produce reasonable results for scour predictions. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and presents an alternative to the regression in the form of artificial neural networks, ANNs, and adaptive neuro-fuzzy inference system, ANFIS. Two ANNs model, feed forward back propagation, FFBP, and radial basis function, RBF, were utilized to predict the depth of the scour hole. Two combinations of input data were used for network training; the first input combination contains six-dimensional variables, which are flow depth, mean velocity, critical flow velocity, grain mean diameter, pile diameter, distance between the piles (gap), besides the number of piles normal to the flow and the number of piles in-line with flow, while the second combination contains seven non-dimensional parameters which is a composition of dimensional parameters. The training and testing experimental data on local scour at pile groups are selected from several precious references. Networks’ results have been compared with the results of empirical methods that are already considered in this study. Numerical tests indicate that FFBP-NN model provides a better prediction than the other models. Also a sensitivity analysis showed that the pile diameter in dimensional variables and ratio of pile spacing to pile diameter in non-dimensional parameters are the most significant parameters on scour depth.     

基于机器学习分类器的DNS拒绝服务攻击的检测系统

阐述了拒绝服务（DoS）对DNS可能构成的威胁,提出了一种能对不同类型DNS的DoS攻击进行检测和分类的入侵检测系统（IDS）。该系统由统计预处理器和机器学习（ML）引擎组成。利用模拟网络对三种神经网络分类器和支持向量机进行了评估。结果表明,BP神经网络引擎以99%的准确率优于其他类型的分类器。     

基于深度神经向量机自回归的协同过滤算法

为发掘卷积神经网络在协同过滤预测中的潜力,针对神经自回归模型方法和支持向量机在深度学习中的优势,提出基于深度神经向量机自回归的协同过滤方法。通过将神经网络最后一层的激发函数替换为线性支持向量回归函数的方式,学习基于最小边缘的对数损失。在多个公开数据集上的实验结果表明,该算法在深度神经自回归对协同过滤问题实现较好预测的基础上,线性向量回归函数的使用能更好地提升预测效果。     

Estimation of individual prediction reliability using the local sensitivity analysis

For a given prediction model, some predictions may be reliable while others may be unreliable. The average accuracy of the system cannot provide the reliability estimate for a single particular prediction. The measure of individual prediction reliability can be important information in risk-sensitive applications of machine learning (e.g. medicine, engineering, business). We define empirical measures for estimation of prediction accuracy in regression. Presented measures are based on sensitivity analysis of regression models. They estimate reliability for each individual regression prediction in contrast to the average prediction reliability of the given regression model. We study the empirical sensitivity properties of five regression models (linear regression, locally weighted regression, regression trees, neural networks, and support vector machines) and the relation between reliability measures and distribution of learning examples with prediction errors for all five regression models. We show that the suggested methodology is appropriate only for the three studied models: regression trees, neural networks, and support vector machines, and test the proposed estimates with these three models. The results of our experiments on 48 data sets indicate significant correlations of the proposed measures with the prediction error.     

最小二乘支持向量机用于水量预测

针对标准支持向量机建模时间长的缺点,为了城市用水量准确预测,需建立有效的预测模型.采用的最小二乘支持向量机基于结构风险最小化,并在支持向量机的基础上,将求解二次规划问题转化线性方程组,采用径向基核函数,使最小二乘支持向量机模型的待定参数比标准支持向量机少,可大大加快建模速度,同时还采用了人工免疫系统的自适应动态克隆选择算法,在寻优过程中能够准确、快速地搜索最小二乘支持向量机的最优参数.把上述模型用于城市日用水量预测,具有学习速度快.也具有良好的非线性建模和泛化能力,而且预测精度较高.     

Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research.In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD1) process, a mixed-modal back propagation neural network (MMBP) module and so on.Testing against data sets of non-redundant protein sequences returned values for the Q₃ accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy.Availability: http://www.kdd.ustb.edu.cn/protein_Web/.     

A Support Vector Machine Based Approach for Forecasting of Network Weather Services

We present forecasting related results using a recently introduced technique called Support Vector Machines (SVM) for measurements of processing, memory, disk space, communication latency and bandwidth derived from Network Weather Services (NWS). We then compare the performance of support vector machines with the forecasting techniques existing in network weather services using a set of metrics like mean absolute error, mean square error among others. The models are used to make predictions for several future time steps as against the present network weather services method of just the immediate future time step. The number of future time steps for which the prediction is done is referred to as the depth of prediction set. The support vector machines forecasts are found to be more accurate and outperform the existing methods. The performance improvement using support vector machines becomes more pronounced as the depth of the prediction set increases. The data gathered is from a production environment (i.e., non-experimental).     

经验模式分解与时间序列分析在网络流量预测中的应用

提出一种经验模式分解和时间序列分析的网络流量预测方法. 首先,对网络流量时间序列进行经验模式分解,产生高低频分量和余量;然后,对各分量进行时间序列分析,确保高频分量采用改进和声搜索算法优化的最小二乘支持向量机模型、低频分量和余量采用差分自回归滑动平均模型进行建模和预测;最后,将预测结果通过RBF神经网络进行非线性叠加,得到最终的预测值.仿真实验表明,所提出方法具有更好的预测效果和更高的预测精度.     

基于改进LS-SVM的随钻测量数据传输误码率预测

针对泥浆连续波随钻测量数据传输误码率预测精度低、数据传输过程中易受干扰信号影响等缺点,提出利用改进的最小二乘向量积(LS-SVM)对连续波数据传输误码率建立预测模型,并引用遗传算法对参数寻优,在建立模型过程中利用狄克逊准则对数据进行筛选,从而提高误码率预测的精度.在小样本数据的情况下,采用Matlab建立基于改进的最小二乘支持向量机泥浆连续波数据传输模型.仿真结果表明该模型能够有效地避免陷入局部最优问题,具有较强的泛化能力和预测能力.通过与误差反传前馈(Back propagation,BP)和Elman神经网络预测模型对比可知,该模型预测精度更高,预测值更接近于实际值,可以用于泥浆连续波数据传输误码率预测.     

Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers

The physical process of scour around bridge piers is complicated. Despite various models presented to predict the equilibrium scour depth and its time variation from the characteristics of the current and sediment, scope exists to improve the existing models or to provide alternatives to them. In this paper, a neural network technique within a Bayesian framework, is presented for the prediction of equilibrium scour depth around a bridge pier and the time variation of scour depth. The equilibrium scour depth was modeled as a function of five variables; flow depth and mean velocity, critical flow velocity, median grain diameter and pier diameter. The time variation of scour depth was also modeled in terms of equilibrium scour depth, equilibrium scour time, scour time, mean flow velocity and critical flow velocity. The Bayesian network predicted equilibrium and time-dependent scour depth much better when it was trained with the original (dimensional) scour data, rather than using a non-dimensional form of the data. The selection of water, sediment and time variables used in the models was based on conventional scour depth data analysis. The new models estimate equilibrium and time-dependent scour depth more accurately than the existing expressions. A committee model, developed by averaging the predictions of a number of individual neural network models, increased the reliability and accuracy of the predictions. A sensitivity analysis showed that pier diameter has a greater influence on equilibrium scour depth than the other independent parameters.     

基于粗糙集-支持向量机的软件缺陷预测

软件缺陷预测已成为软件工程的重要研究课题,构造了一个基于粗糙集和支持向量机的软件缺陷预测模型。该模型通过粗糙集对原样本集进行属性约减,去掉冗余的和与缺陷预测无关的属性,利用粒子群对支持向量机的参数做选择。实验数据来源于NASA公共数据集,通过属性约减,特征属性由21个约减为5个。实验表明,属性约减后,Bayes分类器、CART树、神经网络和本文提出的粗糙集—支持向量机模型的预测性能均有所提高,本文提出的粗糙集支持向量机的预测性能好于其他三个模型。     

基于模糊信息粒化与混合神经网络的混沌时间序列预测

为了提高对混沌时间序列预测的精准度,提出了一种基于模糊信息粒化和注意力机制的混合神经网络预测模型。首先对数据进行归一化处理,利用模糊信息粒化对数据的复杂度进行简化;然后将经过相空间重构后的样本输入卷积神经网络(CNN)提取空间特征;再利用长短期记忆神经网络(LSTM)进一步提取时间特征;最后将融合特征传递给注意力机制提取关键特征,得出预测结果。选取Logistic、洛伦兹和太阳黑子混沌时间序列进行实验,并与CNN-LSTM-Att模型、CNN-LSTM模型、FIG-CNN模型、FIG-LSTM模型、CNN模型、LSTM模型、支持向量机(SVM)及误差逆传播(BP)模型进行对比分析。结果表明,所提的预测模型预测精度更高,误差更小。