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- W2069388972 abstract "This study applies a novel neural network technique, support vector regression (SVR), to rainfall forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as particle swarm optimization algorithm (SVR--PSO), which searches for SVR’s optimal parameters, and then adopts the optimal parameters to construct the SVR models. The monthly rainfall in the Guangxi of China during 1954--2008 were employed as the data set. The experimental results demonstrate that SVR--PSO outperforms the SVR models based on the normalized mean square error (NMSE) and mean absolute percentage error (MAPE)." @default.
- W2069388972 created "2016-06-24" @default.
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- W2069388972 date "2010-01-01" @default.
- W2069388972 modified "2023-09-27" @default.
- W2069388972 title "Support Vector Regression Based on Particle Swarm Optimization for Rainfall Forecasting" @default.
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- W2069388972 doi "https://doi.org/10.1109/cso.2010.154" @default.
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