An application of artificial neural networks for rainfall forecasting

✍ Scribed by Kin C. Luk; J.E. Ball; A. Sharma

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 963 KB
• Volume: 33
• Category: Article
• ISSN: 0895-7177
• DOI: 10.1016/s0895-7177(00)00272-7

✦ Synopsis

Rainfall forecasting ia important for many catchment management applications, in particular for flood warning systems. The variability of rainfall in spsce and time, howeve r, renders quantitative forecasting of rainfall extremely difIicult. The depth of rainfall and its diiribution in the temporal and spatial dimensions depends on many variables, such ss pressure, temperature, and wind speed and direction. Due to the complexity of the atmospheric proceesea by which rainikll is generated and the lsck of available data on the necessary temporal and spatial scales, it is not fessible generally to forecest rainfall using a physically based process model. Recent developments in artificial intellllence and, in particular, these techniques aimed at pattern recognition, however, provide en altemstive approach Tar developing of a rainfall forecasting model. Artlflclal neural networks (ANNE), which perform a nonlinear mapping between inputs and outputs, are one such technique. Preeented in thii paper m the reeults of a study investigst~g the application of ANNE to forecast the spatial distribution of rainfall for an urban catchment. Three alternative types of ANNE, namely multilayer feedforward neural networks, partial recurrent neural networks, and time delay neural networks, were identified, developed and, ss presented in this paper, found to provide reasonable predictions of the rainfall depth one time-step iu advance. The data requirements. for and the accuracy obtainable from these three alternative types of ANNs are discussed.