Monthly Precipitation Prediction by Artificial Neural Networks (Case study: Mashhad synoptic station) Prévision de précipitations mensuelles à l'aide de réseaux de neurones artificiels (étude de cas de la station synoptique de Mashhad)

@inproceedings{Khodashenas2010MonthlyPP,
  title={Monthly Precipitation Prediction by Artificial Neural Networks (Case study: Mashhad synoptic station) Pr{\'e}vision de pr{\'e}cipitations mensuelles {\`a} l'aide de r{\'e}seaux de neurones artificiels ({\'e}tude de cas de la station synoptique de Mashhad)},
  author={Saeed Reza Khodashenas and Nasser Khalili and Kambiz Davari},
  year={2010}
}
Several ANN models were developed to prediction of monthly precipitation data in Mashhad synoptic station. From the total 636 monthly precipitation data (from 1958 to 2008), 580 data has been used for training networks and the rest selected randomly has been used for validation of the models. To extract the precipitation dynamic of this station by ANN, a new approach of three-layer feed-forward perceptron network with back propagation algorithm was used. The sensitivity of the prediction… CONTINUE READING

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