Integrating hydrometeorological information for rainfall-runoff modelling by artificial neural networks

✍ Scribed by Yen-Ming Chiang; Fi-John Chang

• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 213 KB
• Volume: 23
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/hyp.7299

✦ Synopsis

The major purpose of this study is to effectively construct artificial neural networks-based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource-derived forecasts (P r ), NWP precipitation forecasts (P n ), and improved precipitation forecasts (P m ) by merging P r and P n . The analysis shows that the accuracy of P m is higher than that of P r and P n . The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4-6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)-based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1-6-h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time-lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1-3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4-6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling.