Linear and Nonlinear Regression Models

Data Mining ◽  
2013 ◽  
pp. 21-29
Author(s):  
Nong Ye
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

2013 ◽  
pp. 273-289
Author(s):  
Jiansheng Wu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Principal Component ◽  
Linear Regression Models ◽  
Rainfall Forecasting ◽  
Forecasting Accuracy ◽  
Parametric Regression ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear ◽  
Artificial Neural Network Ann

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

2011 ◽  
Vol 2 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Jiansheng Wu
Keyword(s):  
Nonlinear Regression ◽  
Regression Models ◽  
Principal Component ◽  
Linear Regression Models ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Accuracy ◽  
Parametric Regression ◽  
Nonlinear Regression Models ◽  
Linear And Nonlinear

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

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