Comparing multiple linear regression and support vector machine models for predicting electricity consumption on pasture based dairy farms

Prediction of soil physical properties by optimized support vector machinesThe potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiple-linear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.

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Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

International Journal of Biometeorology ◽

10.1007/s00484-017-1454-6 ◽

2017 ◽

Vol 62 (3) ◽

pp. 459-469 ◽

Cited By ~ 13

Author(s):

Philip G. Oguntunde ◽

Gunnar Lischeid ◽

Ottfried Dietrich

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Multiple Linear Regression ◽

Rice Yield ◽

Support Vector ◽

Climate Variables ◽

Southwest Nigeria ◽

Machine Analysis

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A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen

Hydrology Research ◽

10.2166/nh.2016.149 ◽

2016 ◽

Vol 48 (5) ◽

pp. 1214-1225 ◽

Cited By ~ 22

Author(s):

Xue Li ◽

Jian Sha ◽

Zhong-liang Wang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Linear Regression ◽

Dissolved Oxygen ◽

Multiple Linear Regression ◽

Coefficient Of Determination ◽

Support Vector ◽

Physical Factors ◽

Data Set ◽

Important Indicator

Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water–air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.

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Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine

RSC Advances ◽

10.1039/c3ra43599g ◽

2013 ◽

Vol 3 (46) ◽

pp. 23924 ◽

Cited By ~ 17

Author(s):

Qiliang ‘Luke’ Wang ◽

Onur G. Apul ◽

Pengfei Xuan ◽

Feng Luo ◽

Tanju Karanfil

Keyword(s):

Neural Network ◽

Carbon Nanotubes ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Linear Regression ◽

Multiple Linear Regression ◽

Aromatic Compounds ◽

Support Vector ◽

Qspr Model ◽

Artificial Neural

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Research on New Nonlinear Method Applied on Coal Calorific Value Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.278-280.915 ◽

2013 ◽

Vol 278-280 ◽

pp. 915-919

Author(s):

Ke Lei Sun ◽

Xiao Juan Zhu ◽

Hua Ping Zhou

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Multiple Linear Regression ◽

Error Compensation ◽

Calorific Value ◽

Analysis Data ◽

Support Vector ◽

Nonlinear Method ◽

Machine Model ◽

Predicted Model

Based on research of the relationship between the industrial analysis of coal composition and the calorific value, a multiple linear regression - support vector machine model for predicting calorific value of coal is put forward. The training sample set is made up of the original industrial analysis data and calorific value. Then the preliminary predicted model is established based on multiple linear regression algorithm. At the same time, error compensation is achieved by the support vector machine amend sub-model. The final predicted value is the sum of the preliminary predicted model output and the error compensation. Experimental results demonstrate that the predicted accuracy of the integrated model is more accurate than the traditional predicted models.

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