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Seasonal Rainfall Prediction Using the Matrix
Decomposition Method
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Hideo Hirose, Junaida Binti Sulaiman, Masakazu
Tokunaga
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14th IEEE/ACIS International Conference
on Software Engineering, Artificial Intelligence, Networking
and Parallel/Distributed Computing (SNPD 2013) July 1-3, 2013,
Honolulu, Hawaii. Studies
in Computational Intelligence (R. Lee (Edi)), Volume 492,
pp.173-185, DOI:
10.1007/978-3-319-00738-0, Springer
2013.
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The
matrix decomposition is one of the most powerful methods in recommendation
systems. In the recommendation system,
even if evaluation values in a matrix where users and items are corresponding
to row and column are provided incompletely,
we can predict the vacant elements of the matrix using the observed
values. This method is applied to a variety of the fields, e.g.,
for movie recommendations, music recommendations, book recommendations,
etc. In this paper, we apply the matrix decomposition method to predict
the amount of seasonal rainfalls. Applying the method to the case
of Indian rainfall data from 1871 to 2011, we have found that the
early detection and prediction for the extreme-value of the monthly
rainfall can be attained.
Using the newly introduced accuracy evaluation criterion, risky,
we can see that the matrix decomposition method using cylinder-type
matrix provides the comparative accuracy to the artificial neural
network result which has been conventionally used. |
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| matrix
decomposition; recommendation system; rainfall prediction;
early detection; risky; artificial neural networks; time
series;
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Times Cited in Web of Science:
Cited in Books:
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