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

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

Jiansheng Wu
Copyright: © 2013 |Pages: 17
ISBN13: 9781466636286|ISBN10: 1466636289|EISBN13: 9781466636293
DOI: 10.4018/978-1-4666-3628-6.ch017
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MLA

Wu, Jiansheng. "An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression." Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation, edited by Wei-Chiang Samuelson Hong, IGI Global, 2013, pp. 273-289. https://doi.org/10.4018/978-1-4666-3628-6.ch017

APA

Wu, J. (2013). An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression. In W. Samuelson Hong (Ed.), Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation (pp. 273-289). IGI Global. https://doi.org/10.4018/978-1-4666-3628-6.ch017

Chicago

Wu, Jiansheng. "An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression." In Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation, edited by Wei-Chiang Samuelson Hong, 273-289. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3628-6.ch017

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Abstract

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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