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Forecasting monthly maximum 5-day precipitation
using artificial neural networks with initial lags
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Junaida Sulaiman, Herdianti Darwis, Hideo
Hirose
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International Symposium on Computational
Intelligence and Design (SCID2013), October 28-29, 2013, Hangzhou,
China.
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days of precipitation are known to cause flood in monsoon-type
countries. Forecasting of daily precipitation helps to predict
the occurrences of rainfall and number of wet days while with a
maximum 5-day precipitation, we can predict the magnitude of precipitation
within a specified period that may signified the precipitation
extremes. This paper describes a method to forecast the trend of
maximum 5-day precipitation (MX5d) in the next month using artificial
neural networks (ANN). The purpose is to predict the trend of maximum
precipitation using a descriptive index outlined by World Meteorological
Organization (WMO). The index is used by WMO for evaluating changes
in precipitation extremes. The analysis of extreme precipitation
trend is important for future prediction of high precipitations
events in the area of interest. ANN is widely applied in the hydrology
field due to non-linearity ability in prediction to non-stationary
and seasonal data. Here, ANN is compared with seasonal autoregressive
integrated moving average (ARIMA) in forecasting next month maximum
5-day precipitation. We have compared ANN with seasonal ARIMA to
measure their performances. Prior to model development, the significant
input lags are determined using linear correlation analysis (LCA)
and stepwise regression method, respectively. The ANN method is
feasible in forecasting precipitation extremes when it is trained
with the particle swarm optimization. |
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neural networks; particle swarm optimization; extreme precipitation;
seasonal autoregressive integrated moving average
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Times Cited in Web of Science:
Cited in Books:
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