A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques

We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM_2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM_2.5 concentration. Our findings demonstrated that the CV-R² of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM_2.5 concentration in sparsely populated areas.     

Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors

The paper presents the application of wavelet transformation and neural network ensemble to the accurate forecasting of the daily average concentration of particulate matter of diameter up to 10 μm (PM₁₀). Few neural predictors are applied: the multilayer perceptron, radial basis function, Elman network and support vector machine as well as one linear ARX model. They are used for prediction in combination with wavelet decomposition, forming many individual prediction results that will be combined in an ensemble. The important role in presented approach fulfills the wavelet transformation and the integration of this ensemble. We have proposed solution applying the additional neural network responsible for the final forecast (integration of all particular prediction results). The numerical experiments for prediction of the daily concentration of the PM₁₀ pollution in Warsaw are presented. They have shown good overall accuracy of prediction in terms of all investigated measures of quality.     

Compression of AVHRR images by wavelet packets

AVHRR is an imager flying on polar meteorological satellites that, due to its spatial resolution (about 1 km at Nadir view), produces a huge amount of data. A method based on the wavelet packet transform is devised to compress AVHRR images. The method is driven by the compression error (application dependent), so that different types of images have the same quality. The best basis wavelet packet is chosen by L₁ norm criterion that was found to be the most suited for the problem at hand. Ability of the compression method to preserve most fine structures of the images even at the highest resolution is demonstrated based on some examples. Comparison with wavelet algorithms is performed.     

基于模糊时序和支持向量机的高速公路SO2浓度预测算法

针对现有SO₂浓度预测方法中存在的污染物来源和影响因素认识不统一、小样本数据敏感、易于陷入局部最优等问题,文中提出了基于模糊时序和支持向量机的高速公路SO₂浓度预测算法,为搭建高速公路环境健康监测系统提供了可靠的理论支持.该方法依据SO₂浓度的季节变动规律,以季节作为时间序列,以24h为粒化窗宽,通过高斯核函数提取原始样本数据的特征值,输入支持向量机训练模型,并利用k重交叉验证法结合网格划分优化模型参数.文中应用该方法建立了SO₂浓度预测模型,并以2014年4月至2015年3月山西省太旧高速公路某监测点SO₂小时浓度监测值为样本数据,在MATLAB平台下应用LIBSVM工具实现了计算过程.结果表明,基于模糊时序和支持向量机的高速公路SO₂浓度预测算法不受机理性理论研究的限制,支持小样本学习,非线性拟合效果好,泛化能力强.     

Gaussian小波SVM及其混沌时间序列预测

为了提高混沌时间序列的预测精度,针对小波有利于信号细微特征提取的优点,结合小波技术和SVM的核函数方法,提出基于Gaussian小波SVM的混沌时间序列预测模型.证明了偶数阶Ganssian小波函数满足SVM平移不变核条件,并构建相应的Gaussian小波SVM.时混沌时间序列进行相空间重构,将重构相空间中的向量作为SVM的输入参量.用Ganssian小波SVM与常用的径向基SVM及Morlet小渡SVM进行对比实验,通过对Chen's混沌时间序列和负荷混沌时间序列的预测,结果表明,Ganssian小波SVM的效果比其他两种SVM更好.     

GARCH prediction using spline wavelet support vector machine

Volatility forecasting is vital important in finance to reduce risk and take better decisions. This paper proposes a spline wavelet support vector machine (SWSVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity model. An admissible spline wavelet kernel is constructed by incorporating the wavelet technique and spline theory into support vector machine (SVM). Since spline wavelet function can yield features that describe the stock time series both at various locations and at varying time granularities, the SWSVM gains the cluster feature of volatility well. Compared with Gaussian kernel in the standard SVM, the applicability and validity of spline wavelet kernel in SWSVM are confirmed through computer simulations and experiments on real-world stock data.     

Meteorological effects on sulphur dioxide concentrations on the area of Turbigo

This paper presents the relationship between meteorological parameters and sulphur dioxide concentrations in the area of Turbigo. A large number of sulphur dioxide sources are located in this area: they are both urban and industrial emissions. Mean half hourly SO₂ measurements were recorded over a three year period at five monitoring stations. Meteorological data were taken by a meteorological station in this network and included mean halfhourly measurements of wind direction and speed, air temperature, atmospheric pressure, relative humidity and rainfall. The SO₂ data were classified by the meteorological parameters, singly and in combination. From the analysis of the effects of various meteorological parameters, wind direction was found to be the parameter best correlated with pollution concentration. Additional results regarding the seasonal cycles of pollution levels are also presented. The work is also based on a set of statistical and graphic techniques.     

Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87 gC/m²/day and an R² of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85 gC/m²/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency (e_max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e_max varied from ∼ 0.86 gC/MJ in grasslands to ∼ 1.56 gC/MJ in deciduous forests, while MOD17 e_max was 0.68 gC/MJ for grasslands and 1.16 gC/MJ for deciduous forests, suggesting that refinements of MOD17 e_max may be beneficial.     

Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM

This paper presents a new load forecasting model based on hybrid particle swarm optimization with Gaussian and adaptive mutation (HAGPSO) and wavelet v-support vector machine (Wv-SVM). Firstly, it is proved that mother wavelet function can build a set of complete base through horizontal floating and form the wavelet kernel function. And then, Wv-SVM with wavelet kernel function is proposed in this paper. Secondly, aiming to the disadvantage of standard PSO, HAGPSO is proposed to seek the optimal parameter of Wv-SVM. Finally, the load forecasting model based on HAGPSO and Wv-SVM is proposed in this paper. The results of application in load forecasts show the proposed model is effective and feasible.     

Forecasting volatility based on wavelet support vector machine

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a multidimensional wavelet kernel function and prove it meeting the mercer condition to address this problem. The applicability and validity of wavelet support vector machine (WSVM) for volatility forecasting are confirmed through computer simulations and experiments on real-world stock data.     

The complex fuzzy system forecasting model based on triangular fuzzy robust wavelet ν-support vector machine

This paper presents a new version of fuzzy wavelet support vector regression machine to forecast the nonlinear fuzzy system with multi-dimensional input variables. The input and output variables of the proposed model are described as triangular fuzzy numbers. Then by integrating the triangular fuzzy theory, wavelet analysis theory and ν-support vector regression machine, a polynomial slack variable is also designed, the triangular fuzzy robust wavelet ν-support vector regression machine (TFRWν-SVM) is proposed. To seek the optimal parameters of TFRWν-SVM, particle swarm optimization is also applied to optimize parameters of TFRWν-SVM. A forecasting method based on TFRWν-SVRM and PSO are put forward. The results of the application in sale system forecasts confirm the feasibility and the validity of the forecasting method. Compared with the traditional model, TFRWν-SVM method requires fewer samples and has better forecasting precision.     

GCPSO优化混合核SVM的地铁车站客流预测

地铁中站点客流量为地铁运营调度部门提供实时调度管理依据。将径向基核函数与多项式核函数线性组合,构建了混合核支持向量回归机（SVM）预测模型。采用基于黄金分割的混沌粒子群（GCPSO）对混合核SVM的参数进行寻优,得到最佳的参数组合。利用该混合核SVM预测广州地铁3号线站点短期客流量。结果表明,GCPSO优化的混合核SVM预测模型对地铁站点的短期客流的预测精度高,预测数据和实测数据拟合良好,相对误差较小,明显优于SVM其他三种预测方法及Elman神经网络预测方法。     

The visual quality recognition of nonwovens using a novel wavelet based contourlet transform

In this paper, a novel wavelet based contourlet transform for texture extraction is presented. The visual quality recognition of nonwovens based on image processing approach can be considered as a special case of the application of computer vision and pattern recognition on industrial inspection. For concreteness, the method proposed in this paper can be divided into two stages, i.e., the feature extraction is solved by wavelet based contourlet transform, which is followed by the grade recognition with support vector machine (SVM). For the texture analysis, we propose a novel wavelet based contourlet transform, which can be considered as a simplified but more sufficient for texture analysis for nonwoven image compared with version of the one introduced by Eslami in theory view. In experiment, nonwoven images of five different visual quality grades are decomposed using wavelet based contourlet transform with ‘PKVA’ filter as the default filter of Laplacian Pyramid (LP) and Directional Filter Bank (DFB) at 3 levels firstly. Then, two energy-based features, norm-1?L ¹ and norm-2?L ², are calculated from the wavelet coefficients at the first level and contourlet coefficients of each high frequency subband. Finally, the SVM is designed to be a classifier to be trained and tested with the samples selected from the feature set. Experimental results indicate that when the nonwoven images are decomposed at 3 levels and 16?L ²s are extracted, with 500 samples to train the SVM, the average recognition accuracy of test set is 99.2 %, which is superior to the comparative method using wavelet texture analysis.     

Classification of power system disturbances using support vector machines

This paper presents an effective method based on support vector machines (SVM) for identification of power system disturbances. Because of its advantages in signal processing applications, the wavelet transform (WT) is used to extract the distinctive features of the voltage signals. After the wavelet decomposition, the characteristic features of each disturbance waveforms are obtained. The wavelet energy criterion is also applied to wavelet detail coefficients to reduce the sizes of data set. After feature extraction stage SVM is used to classify the power system disturbance waveforms and the performance of SVM is compared with the artificial neural networks (ANN).     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

Mixed-norm linear support vector machine

This paper presents a new version of support vector machine (SVM) named l ₂ ? l _p SVM (0 < p < 1) which introduces the l _p -norm (0 < p < 1) of the normal vector of the decision plane in the standard linear SVM. To solve the nonconvex optimization problem in our model, an efficient algorithm is proposed using the constrained concave–convex procedure. Experiments with artificial data and real data demonstrate that our method is more effective than some popular methods in selecting relevant features and improving classification accuracy.     

基于多元线性回归的雾霾预测方法研究

提出了一种在线样本更新的多元线性回归分析的雾霾预测方法。首先搜集了北京市天气状况,包括平均气温、湿度、风级等气象数据以及PM2.5、CO、NO₂、SO₂等大气成分浓度数据,然后通过散点图对这些因素进行主要影响因素分析,筛选出对雾霾影响比较明显的因素作为雾霾预测的依据。通过在线样本更新的多元线性回归建立了PM2.5含量预测模型,并将气象要素作为雾霾的判断标准。最后给出实际例子,利用多元线性回归对北京未来一天、三天及一周的PM2.5含量进行较为精确的预测。     

Support vector machine with adaptive parameters in financial time series forecasting

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.     

支持向量机和遗传算法的人脸识别方法

支持向量机是一种基于小样本学习的有效工具,作为分类器被认为具有很高的推广性能,无需先验知识。但是参数的选取与支持向量机的识别性能是相关的,核函数参数σ²和惩罚因子C对支持向量机识别性能会产生很大的影响。针对支持向量机在人脸识别问题中的应用,提出了一种基于遗传算法（GA）的参数选择优化方法。利用笔者曾提出的基于小波分解和积分投影的人脸特征提取算法对人脸图像进行特征参数提取,然后利用优化的支持向量机进行识别。实验结果表明,该方法是有效的。     

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R²), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R²_BANN=0.9278, R²_GBANN=0.9270) are superior to a conventional ANN model (R²_ANN=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R²_WBANN=0.9397, R²_WGBANN=0.9528).