A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

DOI10.1142/S1469026810002793zbMath1207.68253MaRDI QIDQ3568488

Long Jin, Jian-Sheng Wu, Ming-zhe Liu

Publication date: 15 June 2010

Published in: International Journal of Computational Intelligence and Applications (Search for Journal in Brave)

zbMATH Keywords

particle swarm optimizationsupport vector regressionrainfall forecastingprojection pursuit technology

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Randomized algorithms (68W20)

Related Items (2)

A SEASONAL AUTO-REGRESSIVE MODEL BASED SUPPORT VECTOR REGRESSION PREDICTION METHOD FOR H5N1 AVIAN INFLUENZA ANIMAL EVENTS ⋮ A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms

Cites Work

• Rainfall forecasting by technological machine learning models
• Projection pursuit
• Modified support vector machines in financial time series forecasting
• Three improved neural network models for air quality forecasting
• An application of artificial neural networks for rainfall forecasting

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