Combination Of Support Vector Regression Research Articles

High-precision Prediction Method of Electric Vehicle Trading Power based on Neural Network

Introduction: Electric vehicles have become a development trend due to their good environmental protection and energy saving, etc. Prediction of electric vehicle charging volume can help relevant departments optimize power supply, service, and construction. Methods: In this paper, the Support Vector Machine (SVM) model and the combined Long Short Term Memory (LSTM) and Support Vector Regression (SVR) prediction model are constructed for the prediction of charging capacity, and simulated by actual trading power data in Hubei Province. Results: The results show that the prediction effect of the two methods is good, and the LSTMSVR algorithm is judged to have better performance and less error in predicting the fluctuation of transaction power. Conclusion: LSTM-SVR can be used as a charging prediction method to provide a reference basis for the power control strategy of electric vehicles charging management platform, which is conducive to the healthy development of electric vehicles industry.

A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load

The estimation of Suspended Sediment Load (SSL) is challenging due to its complex nature within the field of hydrology. The selection and reduction of input feature dimensions, along with the non-uniformity of the sediment and estimation accuracy, pose challenges when estimating suspended sediment load. By combining support vector regression models with MOGWO, MOPSO, and MOOTLBO, this study aimed to predict suspended sediment load. Hybrid models are constructed with three key objectives: enhancing accuracy, minimizing feature count, and optimizing sediment classification. In order to accomplish this, two scenarios have been formulated. The first scenario prioritizes performance accuracy, whereas the second scenario assigns equal importance to three objectives. The study focuses on the Kosar Dam watershed in southwest Iran. The CHIRPS precipitation product and the GLDAS soil moisture product are considered to be predictors. The extraction of input features is carried out utilizing Principal Component Analysis (PCA). The results from both scenarios indicated that the optimal sediment classification consists of 10 categories, demonstrating superior performance across all three integrated models. Incorporating feature selection enhances the model's performance and decreases the number of features. In the first scenario, 10 features are selected from a set of 30 input feature vectors, while in the second scenario, 14 features are chosen. Consequently, hybrid models prove to be effective in reducing input features, optimizing classification of SSL, and enhancing prediction accuracy. In general, the SVR-MOOTLBO model exhibits superior performance when compared to other models. The performance indices (R, RMSE, MAE, PBIAS, and MD) exhibit variations ranging from 0.02% to 0.75% between the first and second scenarios in SVR-MOOTLBO, while the RPIQ index shows a relatively modest difference of 6.2%. In both scenarios, the SVR parameters are well-tuned, and the search agents of the MOOTLBO algorithm exhibit effective functionality.

Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms

Three-dimensional (3D) printing in the construction industry is growing rapidly due to its inherent advantages, including intricate geometries, reduced waste, accelerated construction, cost-effectiveness, eco-friendliness, and improved safety. However, optimizing the mixture composition for 3D-printed concrete remains a formidable task, encompassing multiple variables and requiring a comprehensive trial-and-error experimentation process. Accordingly, this study used seven machine learning (ML) algorithms, including support vector regression (SVR), decision tree (DT), SVR-Bagging, SVR-Boosting, random forest (RF), gradient boosting (GB), and gene expression programming (GEP) for forecasting the compressive strength (CS) of 3D printed fiber-reinforced concrete (3DP-FRC). For model development, 299 data points were collected from experimental studies and split into two portions: 70% for model training and 30% for model validation. Various statistical metrics were employed to examine the accuracy and generalizability of the established models. The DT, RF, GB, and GEP models demonstrated higher accuracy in the validation set, achieving correlation (R) values of 0.987, 0.986, 0.986, and 0.98, respectively. The DT, RF, GB, and GEP models exhibited mean absolute error (MAE) scores of 4.644, 3.989, 3.90, and 5.691, respectively. Furthermore, the combination of SVR with boosting and bagging techniques slightly improved the accuracy compared to the individual SVR model. Additionally, the SHapley Additive exPlanations (SHAP) approach unveils the proportional significance of parameters in influencing the CS of 3DP-FRC. The SHAP technique revealed that water, silica fume, superplasticizer, sand content, and loading directions are the dominant parameters in estimating the CS of 3DP-FRC. The SHAP local interpretability unveils the intrinsic relationship between diverse input variables and their impacts on the strength of 3DP-FRC. The SHAP interpretability offers significant insights into the optimum mix proportion of 3DP-FRC.

Prediction of refractive error and its progression: a machine learning-based algorithm

ObjectiveMyopia is the refractive error that shows the highest prevalence for younger ages in Southeast Asia and its projection over the next decades indicates that this situation will worsen. Nowadays,...

Enhancing the drilling efficiency through the application of machine learning and optimization algorithm

This article presents a novel Artificial Intelligence (AI) workflow to enhance drilling performance by mitigating the adverse impact of drill-string vibrations on drilling efficiency. The study employs three supervised machine learning (ML) algorithms, namely the Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Regression Decision Tree (DTR), to train models for bit rotation (Bit RPM), rate of penetration (ROP), and torque. These models combine to form a digital twin for a drilling system and are validated through extensive cross-validation procedures against actual drilling parameters using field data.The combined SVR - Bit RPM model is then used to categorize torsional vibrations and constrain optimized parameter selection using the Particle Swarm Optimization block (PSO). The SVR-ROP model is integrated with a PSO under two constraints: Stick Slip Index (SSI<0.05) and Depth of Cut (DOC<5 mm) to further improve torsional stability. Simulations predict a 43% increase in ROP and torsional stability on average when the optimized parameters WOB and RPM are applied. This would avoid the need to trip in/out to change the bit, and the drilling time can be reduced from 66 to 31 h.The findings of this study illustrate the system's competency in determining optimal drilling parameters and boosting drilling efficiency. Integrating AI techniques offers valuable insights and practical solutions for drilling optimization, particularly in terms of saving drilling time and improving the ROP, which increases potential savings.

Fracture toughness evaluation of ground granulated blast furnace slag concrete using experimental study and machine learning techniques

The relationship between mix design and fracture properties of ground granulated blast furnace slag (GGBFS) concrete is complex and influenced by many factors, such as mixture composition. This study evaluates the fracture toughness under mode-I, mode-II, and mixed-mode, performed on centrally straight-notched Brazilian discs. An experimental program generated an extensive database of the fracture toughness of GGBFS mortar, consisting of 143 specimens with different mix designs under different loading angle conditions. Then, a combination of support vector regression (SVR) algorithm and genetic algorithm (GA) as hyperparameter tuner was conducted to develop models for fracture toughness. The SVR algorithm was trained with Radial Basis Function (RBF), polynomial, sigmoid, and linear kernel functions. Based on the Taylor diagram, the models demonstrated robust performance in predicting fracture toughness. The performance metrics indicate that the RBF function is the most reliable model, with R2-score 0.95 and 0.94 for KIC and KIIC, respectively. The importance score of input features was determined with the permutation importance function. The angle between the crack and loading direction is the most influential parameter in the models.

A hybrid SVR-BO model for predicting the soil thermal conductivity with uncertainty

This study proposes a generalised framework for developing a hybrid machine learning (ML) model that combines support vector regression (SVR) with hyperparameter optimisation to predict thermal conductivity ( k) with uncertainty. The framework contains four phases: data pre-processing, determining the best-performing hybrid model, selecting the optimal input combination, and uncertainty implementation. A database containing 2197 data points is first compiled to train the ML model. Three hyperparameter optimisation algorithms are adopted to tune hyperparameters, and their performance is evaluated by model evaluation metrics. Results show that SVR with Bayesian optimisation (SVR-BO) is the best-performing model since it produces more accurate predictions for k than models that employ grid and random searches. Given the sample insufficiency issue encountered in practice, the SVR-BO models with 144 input combinations are analysed. The compassion among models under various input combinations indicates that incorporating temperature as an additional input can provide moderate improvement in the accuracy and generalisability of the hybrid model. Based on the comparison, a five-input model is selected as the best candidate to implement the uncertainty evaluation for k. Results demonstrate that the predicted k possesses higher reliability for denser datasets and shows promising potential for applications in k with uncertainty assessments.

VIX futures pricing based on high‐frequency VIX: A hybrid approach combining SVR with parametric models

AbstractWe propose a novel hybrid approach for volatility index (VIX) futures pricing by combining support vector regression (SVR) with parametric models. Realized semivariances calculated based on high‐frequency VIX are used to characterize the asymmetric shocks of VIX, and the direct pricing framework of the heterogeneous autoregressive model is extended by incorporating realized semivariances. VIX futures prices are first obtained via parametric models, then the predicted prices and realized semivariances are input into SVR to obtain the final predicted values. Empirical results indicate that the combination of SVR with parametric models significantly improves the pricing ability. This indicates the important information of high‐frequency VIX and the necessity of combining machine learning methods with parametric models to obtain more accurate predictions.

Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms

Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms

Improving the Detection Accuracy of Underwater Obstacles Based on a Novel Combined Method of Support Vector Regression and Gravity Gradient

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the combined support vector regression (SVR) algorithm. First, the gravity gradient difference ratio (GGDR) equation, which is only related to the obstacle’s position, is obtained based on the gravity gradient equation by using the difference and ratio methods. Aiming at solving the shortcomings of the GGDR equation based on Newton–Raphson method (NRM), combined with SVR algorithm, a novel SVR–gravity gradient joint method (SGJM) is proposed. Second, the differential ratio dataset is constructed by simulating the gravity gradient data generated by obstacles, and the obstacle location model is trained using SVR. Four measuring lines were selected to verify the SVR-based positioning model. The verification results show that the mean absolute error of the new method in the x, y, and z directions is less than 5.39 m, the root-mean-square error is less than 7.58 m, and the relative error is less than 4% at a distance of less than 500 m. These evaluation metrics validate the reliability of the novel SGJM-based detection of underwater obstacles. Third, comparative experiments based on the novel SGJM and traditional NRM were carried out. The experimental results show that the positioning accuracy of x and z directions in the obstacle’s position calculation based on the novel SGJM is improved by 88% and 85%, respectively.

Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models

Monitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.

Evaluation of AquaCrop and intelligent models in predicting yield and biomass values of wheat.

AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values ​​for the farms were 0.033 and 0.069t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs.

Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye.

Accurate prediction of evapotranspiration values is important in planning agricultural irrigation, crop growth research, and hydrological modeling. This study is aimed at estimating monthly evapotranspiration (ET) values in Hakkâri province by combining support vector regression, bagged tree, and boosted tree methods with wavelet transform. For this purpose, precipitation, runoff, surface net solar radiation, air temperatures, and previous ET values were divided into sub-signals with various mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as input to machine learning (ML) algorithms. The study's main contribution to the literature is to reveal which wavelet-based machine learning model, mother wavelet type, and combination of meteorological data show the most realistic results in ET estimation. While establishing the models, the data were divided into 80% training and 20% testing. The models' performances were based on the widely used root mean square error, mean absolute error, determination coefficient, and Taylor diagrams. As a result of the study, it was revealed that the hybrid wavelet ML, which is established with input combinations separated into subcomponents by wavelet transform, generally produces more successful predictions than the stand-alone ML model. In addition, it was revealed that the optimum ET forecasting model was obtained with the wavelet bagged tree algorithm with Symlet 2 mother wavelet. Even though the best model established is based on the precipitation and temperature inputs, it was revealed that past ET, solar radiation, and runoff values are also effective inputs in ET prediction. The results can also be used in other regions of the world with semi-arid climates, such as Hakkâri. The study's outputs provide essential resources to decision-makers and planners to manage water resources and plan agricultural irrigation.

Empirical Learning Function for Combining Support Vector Regression and Subset Simulation in Structural Reliability Analysis

Empirical Learning Function for Combining Support Vector Regression and Subset Simulation in Structural Reliability Analysis

Predicting Rainfall-induced Landslide Using Bee Colony Algorithm Based on Support Vector Regression

Objective: Natural disasters caused by landslides have done great harm to agricultural production, people's lives, and property. Considering the slope disaster caused by heavy rainfall, it is important to establish an early warning system to monitor rainfall disaster prevention. Huafang University Slope Sustainable Development Research Center (HUSSDRC) has set up a meteorological station equipped with many sensors to provide early warning for landslides in Taiwan. Since the amount of data collected will soon become very large, there is a need to implement strong parallel frameworks containing information from the meteorological station and the displacement of tiltmeters required to predict the landslides caused by rainfall. Apache Spark (AS) is a general framework that contains the parallel process engine for data analytics. In this study, a hybrid method is utilized to predict rainfall-induced landslides. The proposed method combines support vector regression (SVR) with an artificial bee colony (ABC) algorithm on the parallel platform of AS. For the proposed method, the RMSE is 0.562, and it is the best value among these compared approaches. Methods: The SVR together with an ABC algorithm is applied to predict rainfall-induced landslides on AS. The AS can perform parallel data analytics in memory to speed up performance. However, it is hard to set up the best parameters for SVR. Thereafter, the ABC algorithm is utilized to search for the best parameters for SVR. Results: Compared with other methods, the proposed method results provide the smallest root mean square error (RMSE) for predicting rainfall-induced landslides. Conclusion: A hybrid method is proposed to predict rainfall-induced landslides. The proposed hybrid method is based on the parallel platform of AS in which SVR predicts the rainfall-induced landslides, and the ABC algorithm adjusts the best values of parameters for SVR. The comparison of RMSE for the method with existing approaches shows that the method indeed has the best value among compared approaches.

Flood mapping based on the combination of support vector regression and Heun’s scheme

In response to the increasing threats of flash flooding, how to generate flood maps in real time has drawn great attention. In this study, a hybrid ML model was developed for flood map generation with multiple lead times based on the combination of the support vector regression (SVR) method and Heun’s scheme for numerical correction, in which the hydraulic simulation results are used for model training while rainfall and flood sensor data are used as input variables (namely, the SVR-RFN model). To evaluate the performance of the hybrid model, two additional SVR models were developed and used as references for comparison—the first one used rainfall as input variables without sensor data (namely, the SVR-R model), and the second one added flood sensor data as input variables without numerical correction (namely, the SVR-RF model). The three models are tested and compared with a hydraulic model for a historical event in Sanyei River Basin, Taiwan. Using the hydraulic model as the benchmark, the hybrid model performed the best by reducing 63% of flood depth error and had the least deterioration of accuracy as lead time increased. The SVR-RF model performed the second by reducing the flood depth errors by 46% whereas the hydraulic model was more accurate than the SVR-R model. In a comparison of flood extents, the hydraulic model showed overestimation, the SVR-R model showed significant underestimation, whereas the two models with sensor data had balanced predictions. The flood maps generated by the hybrid model were applied for dike safety evaluation and showed a good capability in forecasting the risk of dike breach.

Calcium Soft Sensor Based on the Combination of Support Vector Regression and 1-D Digital Filter for Water Quality Monitoring

Calcium Soft Sensor Based on the Combination of Support Vector Regression and 1-D Digital Filter for Water Quality Monitoring

State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm

The data-driven method is used widely to estimate the state of health (SOH) of the battery, but the selection of data features and the data training methods affect the estimation results greatly. With the stacking algorithm, this paper proposes a multi-feature fusion model to estimate battery SOH by fusing different feature parameters and combining support vector regression (SVR) and long short-term memory network (LSTM). The feature parameters were extracted only from the current change curve of the constant voltage charging stage. The support vector regression based on grid search (GS-SVR) was selected as the primary-learner, and the primary SVR models were constructed through 5-fold cross-validation for different feature parameters. The LSTM was selected as the secondary-learner. With the stacking algorithm, LSTM was used to fuse multiple primary SVR models to form an ensemble learner model to improve the performance of multi-feature fusion. The battery aging test data set and NASA battery test data set were used to evaluate the effectiveness. The results verified the validity and superiority of the proposed method. Compared with the existing estimation methods, root mean square error is reduced by at least 0.11, and mean absolute percentage error is reduced by at least 0.12%.

Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations

Punching shear strength (PSS) is an important index for the design and analysis of two-way reinforced concrete slabs. To accurately predict the PSS of two-way reinforced concrete slabs, a hybrid PSO-SVR model, which is the combination of support vector regression (SVR) and particle swarm optimization (PSO) algorithm was employed on 218 datasets with six design parameters as input variables and PSS as output. Moreover, the feature importance and the sensitivity analysis were performed to analyze quantitatively the feature importance and effect of the design parameters on the PSS. The results showed that, compared with the RMSE = 347.349, MAE = 210.019, and R 2 = 0.916 of the original SVR model, the PSO-SVR model reached better prediction performance with RMSE = 187.958, MAE = 135.889, and R 2 = 0.942. Among the six key design parameters, the effective depth of the slab D and the slab thickness H are the two main important factors that can cause large dispersion of the PSS in a stochastic environment and should be given more attention in the design and construction.

Remaining useful life prediction for aircraft engines based on phase space reconstruction and hybrid VNS-SVR model

Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance.