A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

• Autorzy: S. Monira Sumi , M. Faisal Zaman , Hideo Hirose
• Języki publikacji: EN

Abstrakty

EN

In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.

Słowa kluczowe

EN

rainfall forecasting; machine learning; multi-model method; pre-processing; model ranking

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: 22
• Numer: 4
• Strony: 841-854

Daty

wydano

2012

otrzymano

2011-11-10

poprawiono

2012-05-11

Twórcy

autor

S. Monira Sumi

• Department of Systems Design and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan

autor

M. Faisal Zaman

• Department of Systems Design and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan
• School of Electronic Engineering, Dublin City University, Glasnevin, Dublin, Ireland

autor

Hideo Hirose

• Department of Systems Design and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, Japan

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Identyfikatory

DOI

10.2478/v10006-012-0062-1