A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms

DOI10.3390/A10020057zbMath1461.62201OpenAlexW2615493097MaRDI QIDQ1662729

Yanan Yu, Yayun Liu, Jinglin Du, Weilan Yan

Publication date: 20 August 2018

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a10020057

zbMATH Keywords

particle swarm optimizationsupport vector machineprecipitation prediction

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to environmental and related topics (62P12) Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

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• Geomechanical parameters identification by particle swarm optimization and support vector machine
• A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Data mining. Concepts and techniques

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