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Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 6 | Page No.: 1098-1105
DOI: 10.3923/jas.2009.1098.1105
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Seasonal Rainfall Forecasting Using Artificial Neural Network

G.A. Fallah-Ghalhary, M. Mousavi-Baygi and M. Habibi-Nokhandan

The rainfall of Khorasan Province, the Northeastern part of Iran, was evaluated from Dec. to May that is included 80% total of annual rainfall in the area under study using artificial neural network. The data of 37 rainfall stations were selected and analyzed over a period of 33 years (1970-2002). The Digital Elevation Model (DEM) was then used to calculate the average rainfall in the area of interest. The relation between variation of synoptic patterns including Sea Surface Temperature (SST), Sea Level Pressure (SLP), the difference of sea level pressure, the difference between sea surface temperature and 1000 hPa surface level, relative humidity at 300 hPa level, geopotential height at 500 hPa level and air temperature at 850 hPa level with mean rainfall of the region were considered. Then the artificial neural network model was trained for 1970-2002 period and rainfall for period of 1993-2002 was predicted. The results showed that artificial neural network method was very successful in predicting rainfall and in more than 70% of years could predict rainfall within acceptable precision. The root mean square error of the model was found to be 41 mm which is considered negligible at yearly level and it is expected that by increasing the number of years of statistical data the precision of the model would increase.
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How to cite this article:

G.A. Fallah-Ghalhary, M. Mousavi-Baygi and M. Habibi-Nokhandan, 2009. Seasonal Rainfall Forecasting Using Artificial Neural Network. Journal of Applied Sciences, 9: 1098-1105.

DOI: 10.3923/jas.2009.1098.1105






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