| 講演名 | 2013-10-18 A Neural Network Model for Forecasting Precipitation Extreme , |
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| 抄録(英) | Several days of precipitation can increase the magnitude of accumulated water in a basin. This can cause the lower area of community and housing over flooded with rainfall water in a short time. Many researchers are using precipitation data for forecasting the number of rainy days in daily, monthly and yearly. However, with a maximum 5-day precipitation, we can predict the magnitude of precipitation within a specified period for example in a month, that may identified as precipitation extremes. Therefore, this study describes a method to forecast the trend of maximum 5-day precipitation in the following month using a hybrid of artificial neural networks (ANN) and particle swarm optimization (PSO). It is important to analyze the trend of extreme precipitation for future prediction of high precipitations events in the area of interest. ANN is widely applied in the hydrology field due to its non-linearity ability to map a non-stationary and seasonal data. Here, we have compared ANN with seasonal autoregressive integrated moving average (ARIMA) to measure their performances in forecasting next month maximum 5-day precipitation. Prior to model development in ANN, the significant input lags are determined using linear correlation analysis (LCA) and stepwise regression method (SLR), respectively. Results showed that ANN method is feasible in forecasting precipitation extremes when it is trained with the particle swarm optimization. |
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| キーワード(英) | artificial neural networks / particle swarm optimization / extreme precipitation / seasonal autoregressive integrated moving average |
| 資料番号 | R2013-65 |
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| 研究会 | R |
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| 開催期間 | 2013/10/11(から1日開催) |
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| 申込み研究会 | Reliability(R) |
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| 本文の言語 | ENG |
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| タイトル（英） | A Neural Network Model for Forecasting Precipitation Extreme |
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| キーワード(1)（和/英） | / artificial neural networks |
| 第 1 著者 氏名（和/英） | / Junaida SULAIMAN |
| 第 1 著者 所属（和/英） | Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology Kawazu |
| 発表年月日 | 2013-10-18 |
| 資料番号 | R2013-65 |
| 巻番号（vol） | vol.113 |
| 号番号（no） | 249 |
| ページ範囲 | pp.- |
| ページ数 | 6 |
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