Improving the accuracy of rainfall prediction using a regionalization approach and neural networks

Mohammad Arab Amiri, Christian Conoscenti, Mohammad Saadi Mesgari


Spatial and temporal analysis of precipitation patterns has become an intense research topic in contemporary climatology. Increasing the accuracy of precipitation prediction can have valuable results for decision-makers in a specific region. Hence, studies about precipitation prediction on a regional scale are of great importance. Artificial Neural Networks (ANN) have been widely used in climatological applications to predict different meteorological parameters. In this study, a method is presented to increase the accuracy of neural networks in precipitation prediction in Chaharmahal and Bakhtiari Province in Iran. For this purpose, monthly precipitation data recorded at 42 rain gauges during 1981-2012 were used. The stations were first clustered into well-defined groupings using Principal Component Analysis (PCA) and Cluster Analysis (CA), and
then one separate neural network was applied to each group of stations. Another neural network model was also developed and applied to all the stations in order to measure the accuracy of the proposed model. Statistical results showed that the presented model produced better results in comparison to the second model.


Artificial Neural Networks (ANN); Chaharmahal and Bakhtiari province; Cluster Analysis (CA); Precipitation

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