Precipitation prediction with neural networks

Bodri, L. (2001) Precipitation prediction with neural networks. Acta Geodaetica et Geophysica Hungarica, 36 (2). pp. 207-216. ISSN 1217-8977

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Dramatic oods occurred in Central Europe in recent summers, Hungary having been seriously affected in its eastern part. Predictive approach based on modeling ood recurrence may be helpful in ood management. Summer oods are typically characterized by saturated catchment due to long-lasting heavy precipitation followed by a sudden extreme rainfall. In present work, an artificial neural network (ANN) models were evaluated for precipitation forecasting. A back propagation neural networks were trained with actual annual and monthly precipitation data from east Hungarian meteorological stations for a time period of 38 years. Predicted amounts are next-year-precipitation and summer precipitation in the next year. The ANN models provided a good with the actual data, and have shown a high feasibility in prediction of extreme precipitation.

Item Type: Article
Subjects: Q Science / természettudomány > QE Geology / földtudományok > QE01 Geophysics / geofizika
Depositing User: Endre Sarvay
Date Deposited: 22 Jul 2018 09:01
Last Modified: 08 Sep 2018 08:19

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