| Application of combined grey forecasting model in the prediction of incoming flow |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2012.03.17 |
| Key Words: GM(1,1) BP artificial neural network Markov chain prediction model inflow Fengjiashan reservior |
| Author Name | Affiliation | | GUO Hua | College of Water Resources and Architectural Engineering, Northuest A & F University, Yangling, Shaanxi 712100, China | | CHENYong | Nanjing Hydraulic Planning Design Institute, Nanjing, Jiangsu 210006, China | | MA Yaoguang | College of Water Resources and Architectural Engineering, Northuest A & F University, Yangling, Shaanxi 712100, China |
|
| Hits: 402 |
| Download times: 305 |
| Abstract: |
| With the combination of gray GM(1,1) model, BP artificial neural network and Markov chain, the in-coming flow to a reservoir was predicted by using the data of past inflow and the Qian river runoff establish combination model. GM(1,1) model mainly prediet trend, the first half and measured data is better, the BP neural network model second half part have volatility and measured data is better, making relative error smallest to establish combination mod-el, and using markov chain predict incoming flow range change. Forecasting infolw of 2001 and 2002 to inspection flow model:the relative error of GM(1,1) model were 0.359 and 0.017; The relative error of BP neural network were 0.032 and 0.251, the relative error model portfolios were 0.164 and 0.117, combined prediction value within the
range. This combination forecasting model is reasonable and effective, and can be more precise forecasts Fengjiashan reservoir inflow. |
|
|
|