Support Vector Regression Based on Particle Swarm Optimization for Rainfall Forecasting

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 SVRs 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).

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

“…The RBF kernel is also effective and has fast training process [52]. For the RBF kernel function, there are three important parameters to be determined [53].

Regularization parameter C : C is parameter for determining the tradeoff cost between minimizing training error and minimizing model complexity.

Kernel parameter ( γ ): γ represents the parameter of the RBF kernel function.

The tube size of e-insensitive loss function ( ε ): ε is the approximation accuracy placed on the training data points.

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