THE APPLICATION OF ANN FOR DOWNSCALING GCMS OUTPUTS FOR PREDICTION OF PRECIPITATION IN ACROSS SOUTHERN IRAN

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AHMADI BASERI N.; SHIRVANI A.; NAZEMOSADAT M.J.

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Journal: Journal of Water and Soil Year:2014 | Volume:28 | Issue:5 Page(s): 1037-1047

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Title

THE APPLICATION OF ANN FOR DOWNSCALING GCMS OUTPUTS FOR PREDICTION OF PRECIPITATION IN ACROSS SOUTHERN IRAN

Author(s)

AHMADI BASERI N. | SHIRVANI A. | NAZEMOSADAT M.J. | Issue Writer Certificate

Keywords

PRECIPITATIONQ2

ACROSS SOUTHERN IRANQ2

DOWNSCALINGQ2

ARTIFICIAL NEURAL NETWORKSQ2

GENERAL CIRCULATION MODELQ2

Abstract

In this study, the ARTIFICIAL NEURAL NETWORKS (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated PRECIPITATION for 25.18o N to 34.51o N and 45o E to 60o E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56o N to 43.25o N and19.68o E to 61.87oE data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly PRECIPITATION data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasastations as the predict and were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multi pleregression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts PRECIPITATION more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated PRECIPITATION is the best predictor in ANN based standardized PRECIPITATION principal components.

Cites

COMPARISON OF ARTIFICIAL NEURAL NETWORK AND SDSM METHODS IN THE DOWNSCALING OF ANNUAL RAINFALL IN THE HADCM3 MODELLING (CASE STUDY: KERMAN, RAVAR AND RABOR)

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APA: Copy

AHMADI BASERI, N., SHIRVANI, A., & NAZEMOSADAT, M.J.. (2014). THE APPLICATION OF ANN FOR DOWNSCALING GCMS OUTPUTS FOR PREDICTION OF PRECIPITATION IN ACROSS SOUTHERN IRAN. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 28(5), 1037-1047. SID. https://sid.ir/paper/141127/en

Vancouver: Copy

AHMADI BASERI N., SHIRVANI A., NAZEMOSADAT M.J.. THE APPLICATION OF ANN FOR DOWNSCALING GCMS OUTPUTS FOR PREDICTION OF PRECIPITATION IN ACROSS SOUTHERN IRAN. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2014;28(5):1037-1047. Available from: https://sid.ir/paper/141127/en

IEEE: Copy

N. AHMADI BASERI, A. SHIRVANI, and M.J. NAZEMOSADAT, “THE APPLICATION OF ANN FOR DOWNSCALING GCMS OUTPUTS FOR PREDICTION OF PRECIPITATION IN ACROSS SOUTHERN IRAN,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 28, no. 5, pp. 1037–1047, 2014, [Online]. Available: https://sid.ir/paper/141127/en

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