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A rainfall forecasting method using machine learning
models and its application to Fukuoka city case
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S. Monira SUMI, M. Faisal ZAMAN, Hideo HIROSE
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International Journal of Applied Mathematics
and Computer Science, Vol. 22, No. 4, pp.841−854, (2012.12) DOI:
10.2478/v10006-012-0062-1
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the present article, an attempt has been made to derive optimal
data-driven machine learning methods for forecasting average daily
and monthly rainfall of Fukuoka city in Japan. This comparative
study has been conducted from three aspects: modelling inputs,
modelling methods and pre-processing techniques. A comparison between
linear correlation analysis and average mutual information is done
to find the optimal input technique. For modelling of the rainfall,
a novel hybrid multi-model method is proposed and compared with
its constituent models. The models are, 1) artificial neural network,
2) multivariate adaptive regression spline, 3) k -nearest neighbour,
and 4) radial basis support vector regression. Each of the above
methods are 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 by a variable
selection technique and the higher ranked models are selected based
on the leave-one-out cross-validation error. The forecasting of
hybrid model is done by the weighted combination of the finally
selected models.
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rainfall forecasting, machine learning,
multi-model method, pre-processing,
model ranking
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Times Cited in Web of Science: 3
Times Cited in Google Scholar: 6
Cited in Books:
Inspec:
WoS: INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER
SCIENCE 巻: 24 号: 2 ページ: 397-404 発行: JUN 2014; INTERNATIONAL JOURNAL
OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 巻: 24 号: 1 ページ: 123-131
発行: MAR 2014; INTERNATIONAL JOURNAL
OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 巻: 24 号: 1 ページ: 133-149
発行: MAR 2014
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