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An application of artificial neural networks for rainfall forecasting. (English) Zbl 0985.86003
Summary: Rainfall forecasting is important for many catchment management applications, in particular for flood warning systems. The variability of rainfall in space and time, however, renders quantitative forecasting of rainfall extremely difficult. The depth of rainfall and its distribution in the temporal and spatial dimensions depends on many variables, such as pressure, temperature, and wind speed and direction. Due to the complexity of the atmospheric processes by which rainfall is generated and the lack of available data on the necessary temporal and spatial scales, it is not feasible generally to forecast rainfall using a physically based process model. Recent developments in artificial intelligence and, in particular, those techniques aimed at pattern recognition, however, provide an alternative approach for developing of a rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are one such technique. Presented in this paper are the results of a study investigating the application of ANNs to forecast the spatial distribution of rainfall for an urban catchment. Three alternative types of ANNs, namely multilayer feedforward neural networks, partial recurrent neural networks, and time delay neural networks, were identified, developed and, as presented in this paper, found to provide reasonable predictions of the rainfall depth one time-step in advance. The data requirements for and the accuracy obtainable from these three alternative types of ANNs are discussed.

86A10 Meteorology and atmospheric physics
68T05 Learning and adaptive systems in artificial intelligence
Full Text: DOI
[1] Hornik, K.; Stinchcombe, M.; White, H., Multilayer feedforward networks are universal approximators, Neural networks, 2, 359-366, (1989) · Zbl 1383.92015
[2] French, M.; Krajewski, W.; Cuykendall, R.R., Rainfall forecasting in space and time using a neural network, Journal of hydrology, 137, 1-31, (1992)
[3] Hsu, K.L.; Gupta, V.; Soroshian, S., Artificial neural network modeling of the rainfall-runoff process, Water resources research, 31, 10, 2517-2530, (1995)
[4] Elman, J.L., Finding structure in time, Cognitive science, 14, 179-211, (1990)
[5] Waibel, A., Modular construction of time-delay neural networks for speech recognition, Neural computation, 1, 39-46, (1989)
[6] Sarle, W.S., Stopped training and other remedies for overfitting, Proceedings of the 27^{th} symposium on the interface of computing science and statistics, 352-360, (1995)
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