A Hybrid Grey Model to Forecast the Annual Maximum Daily Rainfall
| Autor: | Pin-Chan Lee, Yong-Jun Lin, Chih-Chiang Chiu, Kuo-Chen Ma |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
021105 building & construction Statistics Exponential smoothing 0211 other engineering and technologies 02 engineering and technology Autoregressive integrated moving average Integral form Residual Fourier series 021101 geological & geomatics engineering Civil and Structural Engineering Mathematics |
| Zdroj: | KSCE Journal of Civil Engineering. 23:4933-4948 |
| ISSN: | 1976-3808 1226-7988 |
| DOI: | 10.1007/s12205-019-0114-2 |
| Popis: | This study proposes a hybrid grey model for forecasting annual maximum daily rainfall in order to determine long-term hydrological system trends. The proposed model uses an integral form of background value to improve accuracy, and applies two residual operators, the Fourier series and the exponential smoothing technique, to correct periodic and stochastic errors. The annual maximum daily rainfall measured by 5 stations around Taiwan are used to validation the proposed model. The performance of the proposed hybrid grey model is compared with those of the autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models. By evaluation of different indicators, it is shown that the proposed model outperforms both compared models. With more precise information, the proposed model will allow government officials and civil engineering-related industries to better prepare for heavy rainfall, averting potential disasters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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