Basis Function Support Vector Regression Research Articles

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Comparison of various machine learning regression models based on Human age prediction

The development of machine learning strategies has made it possible to diagnose some disease automatically based on data obtained from medical imaging. Brain age is one of the factors that can be used as an indicator of cognitive well-being. Recent advancements in machine learning have made it possible for computers to anticipate classification and prediction outcomes more accurately than humans. In this study, five widely used machine learning  regression models (Linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), relevance vector regression (RVR), Elastic Net and Gaussian process regression (GPR)) were trained and evaluated to predict brain age using volumes of brain regions data. Moreover, a dimensionality reduction technique was utilized to reduce the dimensionality of the input feature space. The data were collected from one hundred and eleven participants. The results showed no performance difference amongst models trained on the same type of data, suggesting that the type of input data had a stronger influence on prediction performance than the model choice. The experimental results indicated that the GPR was the best fit model (R2=0.57, R=0.75) among the other regression models while the G-SVR was the worst fit model (R2=0.0006, R=0.025) with such number of the input data. 

Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil

Measurement of gas diffusion coefficient (Dp) of biochar-amended soil (BAS) under varying conditions is essential for assessing the adsorption capacity and water/gas diffusion in compacted BAS. However, there is no established equation of Dp available on this topic. Also, the factors influencing gas diffusion in BAS have not been properly studied and remain unclear. Various machine learning models were employed in this paper to learn and predict the Dp of BAS based on experimental data. Six factors (i.e., degree of compaction (DOC), biochar content (BC), soil air content (SAC), gravimetric water content (GWC), degree of saturation (DS), and porosity) are considered for testing the prediction models. The epsilon radial basis function support vector regression model showed better accuracy and predictive performance (R=0.9925) than other models and was further improved by applying the feature selection technique using the multiple linear regression and tree-based models (R=0.9937). The results reveal that SAC, DS, and porosity are the main predictor variables. The SAC proved to be the most influential predictor variable based on the estimated p-value. Furthermore, the optimal Dp was established for the various DOC and BC, which could be useful in designing engineered landfill covers. The accurate model prediction and relative importance of the predictor variables could significantly minimize the experimental work volume required to determine Dp, thereby saving time and cost.

A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge / Um estudo comparativo sobre algoritmos de regressão de aprendizagem de máquinas aplicado à modelagem de centrífugas a gás

The gas Centrifuge is a very hard equipment to model, because it involves a gas dynamic with many complications, such as hypersonic waves and rarefied regions combined with continuous flow areas. Therefore, data analysis regressions remain currently a very important technique to understand and describe the problem in a practical way. This paper intends to apply and compare several regression techniques using machine learning, to obtain a hydraulic and a separative power model of gas centrifuge used in enrichment plants. For this purpose, a set of normalized data composed of 134 experimental lines was used, observing the variables of interest, the separation power (dU), and the waste pressure (Pw), through the following explanatory variables: feed flow (F), cut (q), and product pressure (Pp). The comparisons were presented between the results obtained for the models generated by the following: algorithms, multivariate regression, multivariate adaptive regression splines – MARS, bootstrap aggregating multivariate adaptive regression splines – Bagging MARS, artificial neural network – ANN, extreme gradient boosting – XGBoost, support vector regression– Poly SVR, radial basis Function support vector regression – RBF SVR, K-nearest neighbors – KNN and Stacked Ensemble. That way, to avoid overfitting and provide insights about generalization of the models in unseen data, during the training phase, the k-fold cross validation approach was used.  Subsequently, the residuals were analyzed, and the models were compared by the following metrics: Root mean square error – RMSE; Mean squared error – MSE; Mean absolute error – MAE; and Coefficient of determination – R2.

Cross-validation research based on RBF-SVR model for stock index prediction

<abstract> The ups and downs of stock indexes are one of the most concerned issues for investors in the stock market. To improve the accuracy of stock index prediction, this paper compares traditional K-Fold Cross-Validation (KCV) and three cross-validation methods in the Radial Basis Function Support Vector Regression (RBF-SVR) model. They are named as Abandon Tail Cross-Validation (ATCV), Sequential Division Cross-Validation (SCV) and Gap Sequential Division Cross-Validation (GSCV). It is found that KCV has very limited validation ability for stock indexes time series data with no certain relevance. However, SCV and GSCV with small gap perform better, with high accuracy about 88% and small error about 2%. This research shows that the establishment of time series forecasting models for stock indexes needs to pay more attention to cross-validation methods, which cannot randomly dividing training set and test set. It is strongly recommended to use SCV and GSCV instead of KCV. In addition, the choice of the penalty parameter C and the radial basis kernel function parameter γ largely determines the accuracy and reliability of RBF-SVR stock index prediction model. </abstract>

A personalized point-of-interest recommendation model via fusion of geo-social information

Recently, as location-based social networks (LBSNs) rapidly grow, general users utilize point-of-interest recommender systems to discover attractive locations. Most existing POI recommendation algorithms always employ the check-in data and rich contextual information (e.g., geographical information and users’ social network information) of users to learn their preference on POIs. Unfortunately, these studies generally suffer from two major limitations: (1) when modeling geographical influence, users’ personalized behavior differences are ignored; (2) when modeling the users’ social influence, the implicit social influence is seldom exploited. In this paper, we propose a novel POI recommendation approach called GeoEISo. GeoEISo achieves three key goals in this work. (1) We develop a kernel estimation method with a self-adaptive kernel bandwidth to model the geographical influence between POIs. (2) We use the Gaussian radial basis kernel function based support vector regression (SVR) model to predict explicit trust values between users, and then devise a novel trust-based recommendation model to simultaneously incorporate both the explicit and implicit social trust information into the process of POI recommendation. (3) We develop a unified geo-social framework which combines users’ preference on a POI with the geographical influence as well as social correlations. Experimental results on two real-world datasets collected from Foursquare show that GeoEISo provides significantly superior performances compared to other state-of-the-art POI recommendation models.

Retraction Note to: Temperature-based estimation of global solar radiation using soft computing methodologies

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max–T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics

Energy dissipation control has long been synthesized addressing the trafficking of wheeled vehicles. Wheel-obstacle collision has attracted the studies more on ride comfort, stability, maneuvering, and suspension purposes. This paper communicates, for the first time, the energy dissipation analysis through tire-obstacle collision that frequently occurs for the wheeled vehicles particularly those of off-road vehicles. To this aim, a soil bin facility equipped with a single wheel-tester is employed considering input parameters of wheel load, speed, slippage, and obstacle height each at three different levels. In the next step, the potential of classic artificial neural networks was appraised against support vector regression with the two kernels of radial basis function and polynomial function. On account of performance metrics, it was revealed that radial basis function based support vector regression is outperforming the other tested methods for the prediction of dissipated energy through tire-obstacle collision dynamics. The details are documented in the paper.

RETRACTED ARTICLE: Temperature-based estimation of global solar radiation using soft computing methodologies

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (Tmax, Tmin, and Tavg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of Tmax, Tmin, and Tavg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using Tmax–Tmin and Tmax as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

Rapid detection of browning levels of lychee pericarp as affected by moisture contents using hyperspectral imaging

Lychee is an important tropical and subtropical fruit. However, the quality of lychee fruit changes easily after harvest and it is difficult to control the process. One of the most significant factors impacting lychee quality seriously is enzymatic browning, which is commonly affected by moisture loss of pericarp during storage. As an emerging technique, hyperspectral imaging (HSI) carries many unique advantages compared to conventional detection methods, providing an innovative tool for quality evaluation of many fruits. The current study focused on exploring the relationship between browning levels of lychee and moisture contents (MC) of pericarp, and developing calibration models for determining browning degree of lychee based on the MC prediction of pericarp using HSI technique. Two sets of optimal wavelengths were selected using regression coefficients (RC) from partial least squares regression (PLSR) and successive projections algorithm (SPA), respectively. Calibration models for determining browning levels of lychee were developed using PLSR, back-propagation neural network (BP-NN) and radial basis function support vector regression (RBF-SVR) algorithms and their performances were compared. The results demonstrated that the RBF-SVR model based on the optimal wavelengths selected by RC had the best performance with coefficients of determination R2 of 0.946 and 0.948, and root mean square error (RMSE) of 0.80% and 0.83% for training and testing sets, respectively, showing browning levels of lychee could be determined by this approach. Finally, the visualization map of lychee with different browning levels was created and distribution of browning degree in a lychee was observed by examining color variation among pixels in the map.

Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion

A quantitative approach was proposed to evaluate anthocyanin content of lychee pericarp using hyperspectral imaging (HSI) technique. A HSI system working in the range of 350–1050nm was used to acquire a 3-D lychee image. Successive projection algorithm (SPA) and stepwise regression (SWR) algorithm were utilized to reduce data dimensionality and search for optimal wavelengths related with anthocyanin content in pericarp. Radial basis function support vector regression (RBF-SVR) was adopted to establish quantitative relationship between hyperspectral image information in two sets of optimal wavelengths and anthocyanin content of pericarp. Finally, in order to improve prediction accuracy, SPA-RBF-SVR and SWR-RBF-SVR models were fused into a single model by radial basis function neural network (RBF-NN) algorithm. The results revealed that the fused model possessed a better performance than either SPA-RBF-SVR or SWR-RBF-SVR models alone, as the fused model showed higher coefficients of determination (R2) of 0.891 and 0.872, and lower root mean square errors (RMSEs) of 0.567% and 0.610% for the training and the testing sets, respectively. Visualization maps based on the fused model were generated to display the anthocyanin distribution within lychee pericarp. This study demonstrates that HSI is capable of predicting and visualizing anthocyanin evolution in the pericarp of lychee during storage.

A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification

An advanced odor filtering and sensing system based on polymers, carbon molecular sieves, micro-ceramic heaters and metal oxide semiconductor (MOS) gas sensor array has been designed for quantitative identification of volatile organic chemicals (VOCs). MOS sensor resistance due to chemical vapor adsorption in filtering material and after desorption are measured for five target VOCs including acetone, benzene, ethanol, pentanal, and propenoic acid at distinct concentrations in between 3 and 500 parts per million (ppm). Two kinds of regression methods specifically linear regression analysis based on least square criterion and kernel function based support vector regression (SVR) have been employed to model sensor resistance with VOCs concentration. Scatter plot and Spearman's rank correlation coefficient (ρ) are used to investigate the strength of dependence of sensor resistance on vapor concentration and to search optimal filtering material for VOCs quantification prior to the regression analysis. Quantitative recognition efficiency of regression methods have been evaluated on the basis of coefficient of determination R2 (R-squared) and correlation values. MOS sensor resistance after vapor desorption with carbon molecular sieve (carboxen–1012) as filtering material results the maximum values of R-squared (R2=0.9957) and correlation (ρ=1.00) between the actual and estimated concentration for propenoic acid using radial basis kernel based SVR method.

A novel approach for prediction of vitamin d status using support vector regression.

BackgroundEpidemiological evidence suggests that vitamin D deficiency is linked to various chronic diseases. However direct measurement of serum 25-hydroxyvitamin D (25(OH)D) concentration, the accepted biomarker of vitamin D status, may not be feasible in large epidemiological studies. An alternative approach is to estimate vitamin D status using a predictive model based on parameters derived from questionnaire data. In previous studies, models developed using Multiple Linear Regression (MLR) have explained a limited proportion of the variance and predicted values have correlated only modestly with measured values. Here, a new modelling approach, nonlinear radial basis function support vector regression (RBF SVR), was used in prediction of serum 25(OH)D concentration. Predicted scores were compared with those from a MLR model.MethodsDeterminants of serum 25(OH)D in Caucasian adults (n = 494) that had been previously identified were modelled using MLR and RBF SVR to develop a 25(OH)D prediction score and then validated in an independent dataset. The correlation between actual and predicted serum 25(OH)D concentrations was analysed with a Pearson correlation coefficient.ResultsBetter correlation was observed between predicted scores and measured 25(OH)D concentrations using the RBF SVR model in comparison with MLR (Pearson correlation coefficient: 0.74 for RBF SVR; 0.51 for MLR). The RBF SVR model was more accurately able to identify individuals with lower 25(OH)D levels (<75 nmol/L).ConclusionUsing identical determinants, the RBF SVR model provided improved prediction of serum 25(OH)D concentrations and vitamin D deficiency compared with a MLR model, in this dataset.

Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils

This paper investigates the potential of two variants of extreme learning machine based regression approaches in predicting the resilient modulus of cohesive soils. Support vector regression was used to compare the performance of the proposed extreme learning machine based regression approaches. The dataset used in this study was derived from literature and consists of 9 input parameters with a total of 891 cases. For testing, two methods i.e. train/test and tenfold cross validation was used. In case of train and test methods, a total of 594 randomly selected cases were used to train different algorithms and the remaining 297 data were used to test the created models. Correlation coefficient value of 0.991 (root mean square error = 3.47 MPa) was achieved by polynomial kernel based extreme learning machine in comparison to 0.990 and 0.990 (root mean square error = 4.790 and 4.290 MPa) by simple extreme learning machine and radial basis kernel function based support vector regression respectively with test dataset. Comparisons of results with tenfold cross validation also suggest that polynomial kernel based extreme learning machine works well in terms of root mean square error and computational cost with the used dataset. Sensitivity analysis suggests the importance of confining stress and deviator stress in predicting the resilient modulus when using with polynomial kernel based extreme learning machine modeling approach.