support vector regression

Providing a robust and reliable predictive model for river sediment discharge is an essential task for several environmental and geomorphological perspectives, including water quality, riverbed engineering sustainability, and aquatic habitats. In this research, a new hybrid intelligent approach based on the support vector regression model approach has been developed to predict river sediment discharge. For this purpose, in this research, two optimization algorithms including firefly and gray wolf were used to model the sediment discharge of the river. In order to model, the statistics and information of the Kashkan river hydrometric station located in Lorestan province were used as a case study in 7 combined scenarios of input parameters in 1403-1373. In order to evaluate the performance of the models, the evaluation criteria of correlation coefficient, root mean square error, average absolute value of error, and Nash Sutcliffe coefficient were used. The results showed that the combined scenarios in the studied models improved the performance of the model. Also, the results of the evaluation criteria showed that the support vector regression model-firefly has a correlation coefficient of 0.970, the root mean error rate (ton/day) is 0.145, the mean absolute error value (ton/day) is 0.080 and the coefficient Nash Sutcliffe has 0.980 in the validation stage. In total, the results showed that the use of intelligent models based on the support vector regression approach can be an effective approach in the sustainability of river engineering.

‎Modeling and efficient estimation of the trend function is of great importance in the estimation of variogram and prediction of spatial data. In this article, the support vector regression method is used to model the trend function. Then the data is de-trended and the estimation of variogram and prediction is done. On a real data set, the prediction results obtained from the proposed method have been compared with Spline and kriging prediction methods through cross-validation.  The criterion for choosing the appropriate method for prediction is to minimize the root mean square of the error. The prediction results for several positions with known values were left out of the data set (for some reason) and were obtained for new positions. The results show the high accuracy of prediction (for all positions and elimination positions) with the proposed method compared to kriging and spline.

‎The high-dimensional data analysis using classical regression approaches is not applicable, and the consequences may need to be more accurate. This study tried to analyze such data by introducing new and powerful approaches such as support vector regression, functional regression, LASSO and ridge regression. On this subject, by investigating two high-dimensional data sets  (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

‎The most popular technique for functional data analysis is the functional principal component approach‎, ‎which is also an important tool for dimension reduction‎. ‎Support vector regression is branch of machine learning and strong tool for data analysis‎. ‎In this paper by using the method of functional principal component regression based on the second derivative penalty‎, ‎ridge and lasso and support vector regression with four kernels (linear‎, ‎polynomial‎, ‎sigmoid and radial) in spectroscopic data‎, ‎the dependent variable on the predictor variables was modeled‎. ‎According to the obtained results‎, ‎based on the proposed criteria for evaluating the goodness of fit‎, ‎support vector regression with linear kernel and error equal to $0.2$ has had the most appropriate fit to the data set‎.

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Estimation of compressional and shear wave velocities is very important in the oil and gas industry. Unlike compressional wave velocity, shear wave velocity is not measured in all wells of a field due to its higher costs. Therefore, using an alternative method that estimates the shear wave velocity at a lower cost and with acceptable accuracy is inevitable. In this study, to estimate the response variable in a well, the correlation of several logs in that well (i.e., acoustic logs, density, neutron porosity, resistivity, gamma ray, dolomite volume, quartz volume, and water saturation) with target log investigated. It was found that the compressional wave velocity, density, dolomite volume, and quartz volume logs are more correlated with shear wave velocity. Therefore, these logs were selected as input features for estimating shear wave velocity using different approaches. In the next step, among the various methods, The estimated values obtained from a method that has the best match with the actual shear wave velocity is introduced as the optimal model. Afterward, it is performed to estimate the shear wave velocity in other wells that do not have a shear wave velocity log. In this paper, multiple regression methods and machine learning algorithms (support vector regression, adaptive Neuro-fuzzy inference system, and deep artificial neural network) were applied to predict the shear wave velocity. In this study, data from seven wells were used. Due to the fact that only in well #7 shear wave velocity has been measured, and in six other wells this feature has not been recorded, this field data limitation has caused the data of well #7 to be divided into training, testing, and validation data. In multiple regression methods (linear and interaction models), support vector regression, and adaptive Neuro-fuzzy inference system, Randomly, 70% of the data has been used for training and 30% for testing, but in the artificial neural network method, Randomly, 70% of the data has been used for training, 15% for validation and 15% for network testing. For all methods, the root means square error and correlation between actual and estimated data are calculated. Linear model, interaction model, support vector regression, adaptive Neuro-fuzzy inference system, and deep artificial neural network have provided 91, 92, 89, 94, and 98% correlation in training data, and 88, 89, 86, 90 and 92% in testing data, respectively. Also, the RMSE for each of the mentioned methods is 125.59, 115.86, 148.23, 84.36, and 80.49 (m/s) in the training data and 139.77, 133.44, 166.03, 126.15, and 98.04 (m/s) in the testing data, respectively. Our results show that deep artificial neural network has provided a better solution than other methods. Hence, in this study deep artificial neural network has been proposed to estimate the shear wave velocity in other blind wells. Moreover, the Castagna empirical model was used to validate the obtained results from the deep artificial neural network in these wells, which show a good fit between the two models.

The background of 3D modeling of fluid inclusion data goes back to use of inverse distance weighting (IDW) method in the Caixiashan Pb and Zn deposit (Sun et al., 2011). This method in spite of having some advantages such as simplicity in basis is associated with disadvantages such as uncertainty in selection of weighting function and ignoring data distribution. Today, new methods have been proposed for estimation including the support vector machine method (Dutta et al., 2010). One of this method’s capabilities is in dealing with small data sets (Dutta, 2006; Zhang et al., 1998). In this study, fluid inclusion thermodynamic parameters have been estimated using support vector regression method. Predictive model of mineralization has been provided acording to 3D models resulted for fluid inclusion data and also assumption of proper thermodynamic conditions for chalcopyrite deposition in the Sungun porphyry copper deposit.

In this study, a total of 173 data sets of fluid inclusions were obtained from 59 locations. This dataset using genetic algorithm method divided into training and testing sets (80% and 20%, respectively). Modeling of fluid inclusion thermodynamic parameters has been done by support vector regression method. The SVR is based on the statistical learning theory and the structural risk minimization.

After preparing and determination of training and test datasets, radial basis kernel function (RBF) was selected in order to estimate and model the fluid inclusion thermodynamic parameters using the support vector regression method. Better functionality was the main reason of using this kernel. In the next step, parameters were needed to be carefully determined to obtain a model with high generalization ability. In this regard, the grid search method with cross validation was used to determine optimal values for the model parameters. Model was then trained using the training dataset and finally evaluated on the test dataset. Then fluid inclusion thermodynamic parameters for each block of deposit were estimated using support vector regression method. According to mineralogical and fluid inclusion studies in the Sungun porphyry copper deposit, it has been determined that chalcopyrite deposition is related to fluids with moderate to high salinity and temperatures of 300-400 °C. The predictive model was prepared based on these conditions and estimated thermodynamic Parameters in block model. In this model, each arbitrary block has been labeled on a scale of 1 to 4 (based on the favorable conditions for chalcopyrite deposition). These labels are possibility index for copper deposition. According to possibility index, proper zones have been determined in 3D model. In order to performance evaluation of support vector regression method, the predictive model was compared with 3D model of copper grade. The results of this comparison showed that prepared predictive 3D model has high consistent with copper grade block model.

In this study, 3D modeling of fluid inclusion data was performed to estimate the thermodynamic parameters affecting mineralization (homogenization and eutectic temperatures and salinity) using support vector regression method to determine potential mineralization points in the area. Using the 3D models, we found the homogenization and eutectic temperatures and fluids salinity (in different ranges of these factors) in the Sungun porphyry copper deposit. To evaluate the 3D modeling efficiency in advancing the exploration process of the porphyry deposits, the conformity between mineralization and thermodynamic variations of the fluid inclusions was investigated and, based on it; a tool called “Predictive Model” was presented for the evaluation of the occurrence of mineralization in different parts of the region. A comparison of the SVR-based predictive model and the copper grade block model shows acceptable conformity in low, medium, and high-grade regions.

Prediction of climate change using the recorded data from reference periods requires precise methods that are able to accurately detect fluctuations and predict changes for future periods. In this study, six multivariate regression models, ANN, SVR, ANFIS, SVM and GP, were investigated and compared for down scaling the daily mean temperature of the Urmia synoptic station, using 26 prediction parameters taken from the fifth IPCC report. The average daily temperature values measured from 12/03/1961 to 20/12/2005 were used for the analysis. 16 of the 26 parameters having a high correlation with the average daily temperature values were selected for all methods using the Pearson correlation test. To investigate the modeling errors, the coefficient of determination, Root Mean Square Error, and effectiveness criteria were used. The results of the evaluation of the accuracy and modeling error showed that among the smart models, GP, ANN, ANFIS and SVM, the Genetic Programming model has the least amount of errors, and in the regression model (multivariate regression and support vector regression) support vector regression has the lowest error rate and the highest accuracy of simulated daily temperature values of the Urmia station. In general, the results of the simulation of the mean daily temperature indicate that regression has better accuracy than smart methods. Since in this study, we only used the data from the Urmia synoptic station, so the results are only valid for this station, and it is not safe to generalize the results for all stations.