scholarly journals A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fanping Zhang ◽  
Huichao Dai ◽  
Deshan Tang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Swarm Optimization ◽  
Svm Model ◽  
Monthly Streamflow

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

2019 ◽  
Vol 7 (1) ◽  
pp. T97-T112 ◽  
Author(s):  
Zhi Zhong ◽  
Timothy R. Carr
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Kernel Functions ◽  
Oil Field ◽  
Support Vector ◽  
Well Log ◽  
Swarm Optimization ◽  
Svm Model

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

scholarly journals Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Tongyao Lin ◽  
Tao Yi ◽  
Chao Zhang ◽  
Jinpeng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Project Management ◽  
Pso Algorithm ◽  
Construction Cost ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Power Substation ◽  
Cost Forecasting

To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management. Accurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project management. However, affected by many factors, the construction cost of a power substation project usually presents strong nonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation project cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a particle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the PCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model parameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost prediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results show that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this model is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects.

Assessing the Freshness of Meat by Using Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

2013 ◽  
Vol 76 (11) ◽  
pp. 1916-1922
Author(s):  
XIAO GUAN ◽  
JING LIU ◽  
QINGRONG HUANG ◽  
JINGJUN LI
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Ph Value ◽  
Optimal Parameter ◽  
Particle Swarm ◽  
Plate Count ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Aerobic Plate Count

To improve the performance of meat freshness identification systems, we present a new identification method based on quantum-behaved particle swarm optimization (QPSO) and the support vector machine (SVM). Fresh pork, beef, mutton, and shrimp samples were stored in a hypobaric chamber for several days, and the conventional indices of meat freshness, including total volatile basic nitrogen content, aerobic plate count, pH value, and sensory scores, were determined to achieve the identification of sample freshness. However, the experiments showed that it was difficult to obtain an ideal freshness assessment by any single physicochemical or sensory property. Therefore, SVM was introduced to use these data to build a freshness model. Furthermore, QPSO was proposed to seek the optimal parameter combination of SVM. The experimental results indicated that the hybrid SVM model with QPSO could be used to predict meat freshness with 100% classification accuracy.

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