Hybrid Of Support Vector Regression Research Articles

Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm

The demand for water resources has increased due to rapid increase of metropolitan areas brought on by growth in population and industrialisation. In addition, the groundwater recharge is being afftected by shifting land use pattern caused by urban development. Using precise and trustworthy estimates of groundwater level is vital for the sustainable groundwater resources management in the face of changing climatic circumstances. In this context, machine learning (ML) methods offer a new and promising approach for accurately forecasting long-term changes in the groundwater level (GWL) without computational effort of developing a comprehensive flow model. In order to simulate GWL, five data-driven (DD) models, including the hybridization of support vector regression (SVR) with two optimisation algorithms i.e., firefly algorithm and particle swarm optimisation (FFAPSO), SVR-FFA, SVR-PSO, SVR and Multilayer perception (MLP), have been examined in the present study. Spatial clustering was utilised to choose four observation wells within Cuttack district in order to study and assess the water levels. Six scenarios were created by incorporating numerous variables, such as GWL in the previous months, evapotranspiration, temperature, precipitation, and river discharge. The goal was to identify the variables that were most efficient in predicting GWL. The SVR-FFAPSO model performs best in GWL forecasting for Khuntuni station, according to the quantitative analysis with correlation coefficient (R) = 0.9978, Nash–Sutcliffe efficiency (NSE) = 0.9933, mean absolute error (MAE) = 0.00025 (m), root mean squared error (RMSE) = 0.00775 (m) during the training phase. It is advised that groundwater monitoring network and data collecting system are strengthen in India for ensuring effective modelling of long-term management of groundwater resources.

Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model

In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag ( FS ) and fly ash ( FA ) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA , that are a hybridization of support vector regression ( SVR ) with improved particle swarm algorithm ( PSO ) and genetic algorithm ( GA ). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA ) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR-PSO has provided the highest accuracy than comparative models. For instance, the SVR − PSO during the testing phase provided fewer root mean square error RMSE with 1.386 MPa, a higher Nash–Sutcliffe model efficiency coefficient ( NE ) of 0.972, and lower uncertainty at 95% ( U 95 ) with 28.776%. On the other hand, the SVR − GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U 95 with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS . Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies.

Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization

Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization

Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.

A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping

Limited groundwater resources and their overexploitation have become major challenges for sustainable development worldwide. In this study, an innovative hybrid approach was proposed to generate a groundwater spring potential map (GSPM) from the Sarab plain located in Lorestan Province, Iran, which includes the new best-worst method (BWM), stepwise weight assessment ratio analysis (SWARA), support vector machine learning method (SVR), Harris hawk optimization (HHO), and bat algorithms (BA). The first step involved the inventory of a map prepared to contain 610 spring locations. Randomly, 70% of the spring points were selected as training data, and the remaining 30% were selected for validation. Based on the review of the literature and available data, thirteen factors were generated as independent variables. The BWM and SWARA methods were used to identify correlations between the occurrence of springs and factors. Finally, using SVR-BA and SVR-HHO hybrid models, potential maps of groundwater springs were generated and then evaluated with receiver operating characteristic (ROC) and several statistical evaluators such as sensitivity, specificity, accuracy, and kappa index. Validation of the training data set showed that the success rates for the SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-BA, and BWM-SVR-HHO models were 92.6%, 93.7%, 95.9%, and 96.4%, respectively. The results revealed that with a small difference, BWM-SVR-HHO performed better in training compared to other models. Evaluation of the prediction rate showed that the values of the area under the ROC curve for SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-HHO, and BWM-SVR-BA were 91.7%, 92.4%, 93.3%, and 94.7%, respectively. According to the results, although all models had excellent performance with more than 90% accuracy, BWM-SVR-BA was more accurate in predicting. The hybrid models presented in this study can be used as an accurate and effective methodology to improve the results of spatial modeling of the probability of groundwater occurrence.

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method.

Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

Simulation of the ultimate conditions of fibre-reinforced polymer confined concrete using hybrid intelligence models

Fibre Reinforced Polymer (FRP) composites can provide efficient enhancements in terms of strength and deformability for concrete structures, in which accurate predictions of FRP confined concrete ultimate conditions is highly essential to maintain safety levels, further structural analysis and members design. In this paper, three novel hybrid intelligence models were proposed based on the hybridization of Support Vector Regression (SVR) model with three bio-inspired optimization algorithms as genetic algorithm (GA), particle swarm optimization (PSO) and Whale optimization algorithm (WOA) for predicting the ultimate conditions of FRP-confined concrete. Moreover, 15 existing empirical relations for the prediction of the ultimate strength and strain of FRP-confined concrete have been comprehensively reviewed. The performances of the empirical models and the proposed hybrid models as SVR-GA, SVR-PSO and SVR-WOA are evaluated and compared based on a large database, including 780 circular FRP-confined concrete specimens, which are collected from the open-source published experiments. By comparing the predicted results based on several statistical indicators, the proposed hybrid SVR-models are generally outperforming the existing empirical relations in terms of accuracy and agreement with the experimental database. SVR-WOA provides superior performances than SVR-PSO, SVR-GA and all existed empirical models. The root mean square error is improved using SVR-WOA by 0.9%, 14.9 % and 37% for the ultimate strain capacity, and 2.7%, 4.6% and 17.3% for the ultimate strength compared to SVR-PSO, SVR-GA and the best empirical relation, respectively.

An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

Least squares support vector regression for solving Volterra integral equations

In this paper, a numerical approach is proposed based on least squares support vector regression for solving Volterra integral equations of the first and second kind. The proposed method is based on using a hybrid of support vector regression with an orthogonal kernel and Galerkin and collocation spectral methods. An optimization problem is derived and transformed to solving a system of algebraic equations. The resulting system is discussed in terms of the structure of the involving matrices and the error propagation. Numerical results are presented to show the sparsity of resulting system as well as the efficiency of the method.

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

Determination of the Velocity of Detonation of Primary Explosives Using Genetically Optimized Support Vector Regression

AbstractChemical explosives are among the available high energy‐dense storage materials with transferable energy to surroundings or adjacent materials during detonation. The effectiveness, ignition energy as well as spark sensitivity of these explosives are governed by the velocity of detonation which needs to be determined before energetic material synthesis purposely to enhance safety and lessen the cost as well as the difficulties associated with material synthesis and evaluation. This present research work proposes hybridization of support vector regression (SVR) and genetic algorithm (GA) for estimation of the velocities of primary explosives for the first time. The performance of the proposed hybrid SVR‐GA is compared with the existing Mohammad Jafari et al. model as well as the results of EXPLO5Code's prediction using four parameters for measuring model's performance. The proposed SVR‐GA model shows superior performance compared with two existing models in the literature. The performance of the proposed SVR‐GA model for explosive velocity estimation strengthens its practical application, circumvents the experimental challenges and minimizes the associated potential risk.

A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

Stencil Printing Optimization using a Hybrid of Support Vector Regression and Mixed-integer Linear Programming

This research proposes an optimization approach to enhance the stencil printing process (SPP) in surface mount printed circuit board (PCB) assembly. Stencil printing behavior is affected by many variables including stencil design, solder paste composition, squeegee speed and pressure, and other environmental conditions. In this research, support vector regression (SVR) model is trained to capture the complex relationships among these variables, based on historical data. A mixed-integer linear programming (MILP) model is proposed to minimize the total absolute predicted deviation of average volume transfer from target. The optimal printing settings are retrieved for different sample problems with low computational cost.

Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index

Support vector regression (SVR) has been successfully applied in various domains, including predicting the prices of different financial instruments like stocks, futures, options, and indices. Because of the wide variation in financial time-series data, instead of using only a single standard prediction technique like SVR, we propose a hybrid model called USELM-SVR. It is a combination of unsupervised extreme learning machine (US-ELM)-based clustering and SVR forecasting. We assessed the feasibility and effectiveness of this hybrid model using a case study, predicting the one-, two-, and three-day ahead closing values of the energy commodity futures index traded on the Multi Commodity Exchange in India. Our experimental results show that the USELM-SVR is viable and effective, and produces better forecasts than our benchmark models (standard SVR, a hybrid of SVR with self-organizing map (SOM) clustering, and a hybrid of SVR with k-means clustering). Moreover, the proposed USELM-SVR architecture is useful as an alternative model for prediction tasks when we require more accurate predictions.

Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting

The electric load forecasting is complicated, and it sometimes reveals cyclic changes due to cyclic economic activities or climate seasonal nature, such as hourly peak in a working day, weekly peak in a business week, and monthly peak in a demand planned year. Hybridization of support vector regression (SVR) with chaotic sequence and evolutionary algorithms has successfully been applied to improve forecasting accuracy, and to effectively avoid trapping in a local optimum. However, it has not been widely explored to employ SVR-based model to deal with cyclic electric load forecasting. This paper will firstly investigate the potentiality of a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), with an SVR model to improve load forecasting accurate performance. In which, the proposed CGASA employs internal randomness of chaotic iterations to overcome premature local optimum. Secondly, the seasonal mechanism will then be applied to well adjust the cyclic load tendency. Finally, a numerical example from an existed reference is employed to compare the forecasting performance of the proposed SSVRCGASA model. The forecasting results show that the SSVRCGASA model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models.

A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored / non-censored (event) data

While the role of survival analysis in medicine has continued to be increasingly essential in making treatment and other health care decisions, the common clinical methods used for performing these analyses, such as Cox Proportional Hazard models and Kaplan-Meier curves, have become antiquated. We have developed a new survival analysis technique of the Evolutionary Programming / Evolutionary Strategies Support Vector Regression Hybrid for censored and non-censored event data. This method provides the benefits of optimized statistical learning theory to be used as a replacement for or in addition to existing survival analysis protocols. The technique was tested on an artificially censored data from a well-known benchmark dataset as well as actual clinical data with encouraging results.