Support Vector Regression Methodology Research Articles

Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

The diameter distribution of trees in a stand provides the basis for determining the stand’s ecological and economic value, its structure and stability and appropriate management practices. Scots pine (Pinus sylvestris L.) is one of the most common and important conifers in Turkey, so a well-planned management schedule is critical. Diameter distribution models to accurately describe the stand structure help improve management strategies, but developing reliable models requires a deep understanding of the growth, output and constraints of the forests. The most important information derived by diameter distribution models is primary data on horizontal stand structure for each diameter class of trees: basal area and volume per unit area. These predictions are required to estimate the range of products and predicted volume and yield from a forest stand. Here, to construct an accurate, reliable diameter distribution model for natural Scots pine stands in the Türkmen Mountain region, we used Johnson’s SB distribution to represent the empirical diameter distributions of the stands using ground-based measurements from 55 sample plots that included 1219 trees in natural distribution zones of the forests. As an alternative, nonparametric approach, which does not require any predefined function, an artificial intelligence model was constructed based on support vector machine methodology. An error index was calculated to evaluate the results. Overall, both Johnson’s SB probability density function with a three-parameter recovery approach and the support vector regression methodology provided reliable estimates of the diameter distribution of these stands.

Estimation of the improved high-performance concrete’s mechanical characteristics using unique regression methods

A unique approach for assessing the compressive strength (CS) of high-performance concrete (HPC) incorporating blast furnace slag (BFS) and fly ash (FA) has been created using support vector regression (SVR) analytics. In order to identify crucial SVR methodology variables that could be adjusted for improved performance, the Henry gas solubility optimization (HGSO) and Cuckoo search optimization (CSO) algorithms were both employed in this study. The recommended methods were developed utilizing 1030 experiments and eight inputs, including the CS as the forecasting objective, admixtures, aggregates, and curing age as the main mix design component. The results were then contrasted with those from related literature. The estimate results suggest that combined HGSO-SVR and CSO-SVR analysis might perform extraordinarily well in estimating. The Root mean square error value for the HGSO - SVR decreased remarkably when compared to the CSO - SVR. As can be seen from the comparisons, the HGSO - SVR that was built beats anything previously published. In conclusion, the suggested HGSO - SVR analysis might be determined as the proposed system for forecasting the CS of HPC improved with FA and BFS.

Tree bark prediction along the bole through the support vector regression technique

Tree bark plays a protective role by surrounding the wood of a tree like a cloak. Due to its chemical composition and the possibility of its use in various fields, such as pharmaceuticals, landscape architecture, etc., tree bark receives much attention having outstanding importance for industrial utilization and markets. Tree bark is considered a valuable forest product, along with the wood volume. Thus, the accurate prediction of the bark quantity that a tree can produce is of utmost importance for the sustainable management of the forests. For this reason, the knowledge of its quantities, further enables the accurate prediction of the plain wood volume that can be produced by the forest, as well. Because of the nonlinear nature of this biological variable, its accurate quantification is a very complicated problem. Artificial intelligent methods have shown the potential to reliably predict biological variables that are non-linear in nature. In this work, the support vector regression methodology, as a nonlinear nonparametric machine learning approach, is tested for the accurate prediction of the tree bark factor in every different height of the tree bole, through easily obtained measurements on trees.

Estimation of Bank Profitability Using Vector Error Correction Model and Support Vector Regression

The research problem defined in this article is the identification and the metrification of the macro-economic drivers of bank profitability of the Bulgarian banks. The aim is to establish the relationship between the macroeconomic factors and the indicators measuring bank profitability using an iterative testing methodology of the Vector Error Correction Model (VECM) and the Support Vector Regression (SVR). The relationships between key indicators of bank profitability - ROE and ROA, are analytically modeled as factor-dependent on nine key macroeconomic indicators. The sample size includes quarterly and monthly data for the recovery period of the banking system after its restructuring in 2014. The empirical results of the VECM form two similar models, which are distinguished based on dependent variable ROA and dependent variable ROE. They show the significance of the established results at high degrees of determination and minimal degrees of standard errors of the cointegration vectors. The analysis of bank profitability has been refined using the SVR methodology, but the optimization of the hyperparameters does not confirm high significance. It is concluded that with increasing regulatory requirements for the formation of buffers against macroprudential risks, banks report a relatively neutral dependence in ROA and ROE on the impact of macroeconomic factors, which is logically offset by effective management decisions and innovations in interbank competition.

Sensitivity analysis and ensemble artificial intelligence-based model for short-term prediction of NO2 concentration

In this study, in the first step, three scenarios with different input combinations are created to implement a sensitivity analysis for hourly NO2 prediction in Columbus City, Ohio. Three classes of inputs including concentration-related data (NO2 concentration at previous time steps and NO2 concentration in the suburban monitoring station), meteorology (wind speed, wind direction, and temperature), and traffic-related data (traffic count, hour of the day, and day of the week) are applied to create three scenarios. Also, the support vector regression methodology is employed to perform the sensitivity analysis. Dominant variables determined in the sensitivity analysis are applied as inputs to three models called feed-forward neural network, support vector regression, as well as classification and regression tree. In the last step, ensemble techniques including simple linear averaging, weighted linear averaging, and nonlinear support vector regression ensemble are proposed to improve the performance of sole models. The results indicate that, in the urban area, in addition to NO2 variations in the previous time step, other variables such as hourly traffic count in freeway loop, suburban NO2 concentration, and hour of the day can affect the NO2 concentration. Further, the values of determination coefficient for the individual models, namely classification and regression tree and feed-forward neural network, are 67 and 81% that the ensemble technique as a post-processing approach enhances the performance of them up to 19% and 5% in the verification steps, respectively.

A practical support vector regression algorithm and kernel function for attritional general insurance loss estimation

Abstract The aim of the paper is to derive a simple, implementable machine learning method for general insurance losses. An algorithm for learning a general insurance loss triangle is developed and justified. An argument is made for applying support vector regression (SVR) to this learning task (in order to facilitate transparency of the learning method as compared to more “black-box” methods such as deep neural networks), and SVR methodology derived is specifically applied to this learning task. A further argument for preserving the statistical features of the loss data in the SVR machine is made. A bespoke kernel function that preserves the statistical features of the loss data is derived from first principles and called the exponential dispersion family (EDF) kernel. Features of the EDF kernel are explored, and the kernel is applied to an insurance loss estimation exercise for homogeneous risk of three different insurers. Results of the cumulative losses and ultimate losses predicted by the EDF kernel are compared to losses predicted by the radial basis function kernel and the chain-ladder method. A backtest of the developed method is performed. A discussion of the results and their implications follows.

Retraction Note to: Support vector regression methodology for prediction of output energy in rice production

Retraction Note to: Support vector regression methodology for prediction of output energy in rice production

Expression of Concern: Estimation of the laser cutting operating cost by support vector regression methodology

Expression of Concern: Estimation of the laser cutting operating cost by support vector regression methodology

Retraction Note to: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with th...

Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries

The performance of a support vector regression (SVR) model with a Gaussian radial basis kernel to predict anthocyanin concentration, pH index and sugar content in whole grape berries, using spectroscopic measurements obtained in reflectance mode, was evaluated. Each sample contained a small number of whole berries and the spectrum of each sample was collected during ripening using hyperspectral imaging in the range of 380–1028 nm. Touriga Franca (TF) variety samples were collected for the 2012–2015 vintages, and Touriga Nacional (TN) and Tinta Barroca (TB) variety samples were collected for the 2013 vintage. These TF vintages were independently used to train, validate and test the SVR methodology; different combinations of TF vintages were used to train and test each model to assess the performance differences under wider and more variable datasets; the varieties that were not employed in the model training and validation (TB and TN) were used to test the generalization ability of the SVR approach. Each case was tested using an external independent set (with data not included in the model training or validation steps). The best R2 results obtained with varieties and vintages not employed in the model’s training step were 0.89, 0.81 and 0.90, with RMSE values of 35.6 mg·L−1, 0.25 and 3.19 °Brix, for anthocyanin concentration, pH index and sugar content, respectively. The present results indicate a good overall performance for all cases, improving the state-of-the-art results for external test sets, and suggesting that a robust model, with a generalization capacity over different varieties and harvest years may be obtainable without further training, which makes this a very competitive approach when compared to the models from other authors, since it makes the problem significantly simpler and more cost-effective.

Support vector regression methodology for estimating global solar radiation in Algeria

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaia city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (\(r^{2}\)), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

This paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System model, GFS) and direct measurements. Data from satellite consists of temperature and humidity profiles at different heights, and TCO measurements the days before the prediction. GFS model provides predictions of temperature and humidity for the day of prediction. Alternative data measured in situ, such as aerosol optical depth at different wavelengths, are also considered in the system. The SVR methodology is able to obtain an accurate TCO prediction from these predictive variables, outperforming other regression methodologies such as neural networks. Analysis on the best subset of features in TCO prediction is also carried out in this paper. The experimental part of the paper consists in the application of the SVR to real data collected at the radiometric observatory of Madrid, Spain, where ozone measurements obtained with a Brewer spectrophotometer are available, and allow the system's training and the evaluation of its performance.

Estimation of the laser cutting operating cost by support vector regression methodology

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

Forecasting of Underactuated Robotic Finger Contact Forces by Support Vector Regression Methodology

Robotic manipulators have very strong nonlinearities. Analytical modeling of the robotic gripper is very challenging task. Therefore in this paper soft computing methods is applied in order to estimate contact forces of the robotic finger. Support vector regression (SVR) with radial and polynomial basis functions and the soft computing methods were used. The primary purpose of this study are in clarification of kinetostatic examining of a new finger mechanism utilizing pseudo-unbending body model. The results show the better prediction accuracy with SVR methodology with radial basis function (RBF).

Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface

In this paper we propose to apply the Support Vector Regression (SVR) methodology to significant wave height estimation using the shadowing effect, that is visible on the X-band marine radar images of the sea surface due to the presence of high waves. One of the main problems of using sea clutter images is that, for a given sea state conditions, the shadowing effect depends on the radar antenna installation features, such as the angle of incidence. On the other hand, for a given radar antenna location, the shadowing properties depend on the different sea state parameters, like wave periods, and wave lengths. Thus, in this paper we show that SVR can be successfully trained from simulation-based data. We propose a simulation process for X-band marine radar images derived from simulated wave elevation fields using the stochastic wave theory. We show the performance of the SVR in simulation data and how SVR outperforms alternative algorithms such as neural networks. Finally, we show that the simulation process is reliable by applying the SVR methodology trained in the simulation-based data to real measured data, obtaining good prediction results in wave height, which indicates the goodness of our proposal.

RETRACTED ARTICLE: Support vector regression methodology for prediction of output energy in rice production

The increase in world population has led to a significant increase in food demand throughout the world, so agricultural policy makers in all countries try to estimate their annual food requirements in advance in order to provide food security for their people. In order to achieve this goal, this study developed a novel predictive model based on the energy inputs employed during the production season. Rice caters more than 30 % of the calorie requirement for the Asian countries. In Iran too rice is one of the most important agricultural products. Therefore, objective of this study was to develop a model based on artificial intelligence for predicting the output energy in rice production. Such a model could help farmers and policy makers. This model employed the polynomial and radial basis function (RBF) as the kernel function for support vector regression (SVR). The input energies from different sources used during rice production were given as the inputs to the model, and the output energy was chosen as the output of the model. In order to achieve generalized performance, SVRpoly and SVRrbf tried to minimize the generalization error bound, instead of minimizing the training error. The results show that the proposed model improves the predictive accuracy and capability of generalization. Results show that SVRs can serve as a promising alternative for existing prediction models.

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

Multisensor data fusion can be considered as a strong nonlinear system. A precise analytical solution is challenging to obtain, thus making it hard to dissect with routine diagnostic systems. Since tried-and-true logical systems are extremely difficult to undertake, soft computing methodologies are deemed having potential for such applications. This paper presents the support vector regression (SVR) methodology for sensor fusion to improve tracking ability. Radial basis function (RBF) and polynomial function are used as SVR kernel functions. The system combines Kalman filtering and soft computing principle, i.e., SVR, to structure an effective information combination method for the target framework. A radar-infrared system is proposed to adapt contextual changes and lessen the dubious unsettling influence of an information estimation from multisensory data. The experimental results show that an improvement in predictive accuracy and generalization capability can be achieved using the SVR with RBF kernel compared with the SVR with polynomial kernel approach.

Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions

AbstractThe Australian Bureau of Meteorology (Bureau) issues operational tropical cyclone (TC) seasonal forecasts for the Australian region (AR) and the South Pacific Ocean (SPO) and subregions therein. The forecasts are issued in October, ahead of the Southern Hemisphere TC season (November to April). Improvement of operational TC seasonal forecasts can lead to more accurate warnings for coastal communities to prepare for TC hazards. This study investigates the use of support vector regression (SVR) models, exploring new explanatory variables and non-linear relationships between them, the use of model averaging, and lastly the integration of forecast intervals based on a bias-corrected and accelerated non-parametric bootstrap. Hindcasting analyses show that the SVR model outperforms several benchmark methods. Analysis of the generated models shows that the Dipole Mode Index, 5VAR index and the Southern Oscillation Index are the most frequently selected as explanatory variables for TC seasonal forecasting in all regions. The usage of ENSOrelated covariates implies that definitions of regions and subregions may have to be updated to achieve optimal forecasting performance. Overall, the new SVR methodology is an improvement over the current linear discriminant analysis models and has the potential to increase accuracy of TC seasonal forecasts in the AR and SPO.

RETRACTED ARTICLE: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR poly and SVR rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.

Support Vector Regression Model for the prediction of Loadability Margin of a Power System

Loadability limits are critical points of particular interest in voltage stability assessment, indicating how much a system can be stressed from a given state before reaching instability. Thus estimating the loadability margin of a power system is essential in the real time voltage stability assessment. A new methodology is developed based on Support Vector Regression (SVR) which is the most common application form of Support Vector Machines (SVM). The proposed SVR methodology can successfully estimate the loadability margin under normal operating conditions and different loading directions. SVR has the feature of minimizing the generalization error in achieving the generalized network over the other mapping methods. In this paper, the SVR input vector is in the form of real and reactive power load, while the target vector is lambda (loading margin). To reduce both mean square error and prediction time in SVR, the kernel type and SVR parameters are chosen determined by using grid search based on 10-fold cross-validation method for the best SVR network. The results of SVRs (nu-SVR and epsilon-SVR) are compared with RBF neural networks and validated in the IEEE 30 bus system and IEEE 118 bus system at different operating scenarios. The results demonstrate the effectiveness of the proposed method for on-line prediction of loadability margins of a power system.