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Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 9 | Page No.: 1786-1790
DOI: 10.3923/jas.2009.1786.1790
 
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Forecasting Precipitation with Artificial Neural Networks (Case Study: Tehran)

M.H. Gholizadeh and M. Darand

Abstract:
Artificial Neural Networks (ANN), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Rainfall forecasting has been a difficult subject due to the complexity of the physical processes involved and the variability of rainfall in space and time. Artificial Neural Networks (ANN), which perform a nonlinear mapping between inputs and outputs, are one of the techniques that are suitable for rainfall forecasting. Multiple perceptron neural networks were trained with actual monthly precipitation data from Tehran station for a time period of 53 years. Predicted amounts are of next-month-precipitation in the next year. The ANN models provided a good fit with the actual data and have shown a high feasibility in prediction of month rainfall precipitation. Combination neural networks with Genetic algorithm showed better results.
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How to cite this article:

M.H. Gholizadeh and M. Darand, 2009. Forecasting Precipitation with Artificial Neural Networks (Case Study: Tehran). Journal of Applied Sciences, 9: 1786-1790.

DOI: 10.3923/jas.2009.1786.1790

URL: https://scialert.net/abstract/?doi=jas.2009.1786.1790

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