A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain

Nieto, P.J. Garcı́a; Fernández, J.R. Alonso; Suárez, Víctor Manuel González; Muñiz, C. Díaz; García–Gonzalo, Esperanza; Bayón, Ricardo Mayo

doi:10.1016/j.amc.2015.03.075

Cited by 23 publications

(17 citation statements)

References 35 publications

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“…The final function can be solved by a set of sample data points called support vectors (s.v.) as follows(de Cos Juez et al, 2010;García Nieto et al, 2015a):…”

mentioning

confidence: 99%

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Kong

Sun

et al. 2017

Marine Pollution Bulletin

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“…The final function can be solved by a set of sample data points called support vectors (s.v.) as follows(de Cos Juez et al, 2010;García Nieto et al, 2015a):…”

mentioning

confidence: 99%

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Kong

Sun

et al. 2017

Marine Pollution Bulletin

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“…Thus, it is necessary to develop an efficient approach to simultaneously meet three optimal parameters. Particle swarm optimization (PSO), which was a good method to optimize SVR model parameters, was used to predict the cyanotoxin content [24]. Therefore, in the study, we adopted the optimization model to predict the dynamic heat supply of central heating system.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Wang

2016

Mathematical Problems in Engineering

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We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

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“…The other is predicted by a combined intelligent algorithm. That is, genetic algorithm [17,21], neural network algorithm [5,11,22,23], support vector machine [24][25][26][27][28], particle swarm algorithm [11,17,25], simulated annealing algorithm [21] and other combinations. In the research results of natural gas consumption forecasting, considering the prediction of uncertainties in natural gas consumption, the concepts of grey theory [17,18], Bayesian average model [19], logistic model [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

Gao

2018

Energies

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With the orderly advancement of ‘China's energy development strategic action plan’, the natural gas industry has achieved unprecedented development. Currently, it is planned that by 2020, China’s natural gas consumption will account for at least 10% of the total primary energy consumption, have an orderly and improved energy structure, and achieved energy-saving and emission-reduction targets. Therefore, the accurate prediction of natural gas consumption becomes significantly important. Firstly, based on the research status of forecasting methods and the factors which affect natural gas consumption, this paper used the particle swarm optimization (PSO) algorithm to obtain the input layer weight, and used the optimized extreme learning machine (ELM) algorithm to obtain the hidden layer threshold; by using PSO-ELM as the base predictor and the AdaBoost algorithm, we have constructed the natural gas consumption integrated learning prediction model. Secondly, from the perspective of different provinces and industries, we deeply analyze the current status of natural gas consumption, and the random forest algorithm is used to extract the core influencing factors of natural gas consumption as the independent variables of the prediction model. Finally, data on China's natural gas consumption from 1995 to 2017 are selected, then the feasibility analysis and comparative analysis with other methods are performed. The results show: 1) Using the random forest algorithm to extract the core influencing factors, economic growth, population, household consumption and import dependence degree are significantly representative. 2) Based on the AdaBoost integrated learning algorithm, transforming the weak predictor with poor prediction effect into a strong predictor with strong prediction effect, compared with PSO-ELM、AdaBoost-ELM and ELM algorithm, with R-Square as 0.9999, Mean Square Error (MSE) as 0.8435，Mean Absolute Error (MAE) as 0.2379, Mean Absolute Percentage Error (MAPE) as 0.0008，effectively validated the significant effect of the AdaBoost-PSO-ELM prediction model. 3) Based on the AdaBoost-PSO-ELM prediction model, predict the natural gas core influencing factors and natural gas consumption in the year of 2018–2030. There is an apparent growth trend in the next 13 years, and the average growth rate of natural gas consumption has reached 7.68%.

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A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain

Cited by 23 publications

References 35 publications

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

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