Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters

Gupta, A.; Vijay, R.; Kushwaha, V.K.; Wate, S.R.; Shiehbeigi, A.; Abbaspour, M.; Soltaniyeh, M.; Hosseinzadeh, F.; Abedi, Z.

doi:10.22059/ijer.2014.814

Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters

Document Type : Original Research Paper

Authors

A. Gupta ¹

R. Vijay ¹

V.K. Kushwaha ¹

S.R. Wate ¹

A. Shiehbeigi ²

M. Abbaspour ³

M. Soltaniyeh ⁴

F. Hosseinzadeh ⁵

Z. Abedi ⁶

¹ Environmental Systems Design and Modeling Division, CSIR - National Environmental Engineering Research Institute, Nagpur 40020, India

² Energy Engineering, Department of Energy & Environment ,Tehran Science and Research Branch, Islamic Azad University, Tehran ,Iran

³ Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

⁴ Department of Chemical Engineering, Sharif University of Technology, Tehran, Iran

⁵ Department of Mathematics, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran

⁶ Department of Energy & Environment, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran

10.22059/ijer.2014.814

Abstract

Numerous studies yet have been carried out on downscaling of the large-scale climate data using
both dynamical and statistical methods to investigate the hydrological and meteorological impacts of climate
change on different parts of the world. This study was also conducted to investigate the capability of feedforward
neural network with error back-propagation algorithm to downscale the provincial segmentation of
Iran (30 provinces) on a daily scale. This model was proposed for the downscaling daily temperature,
precipitation and wind speed data, and it was calibrated and verified by using the daily outputs derived from
the National Center for Environmental Prediction (NCEP) database including air temperature, air pressure,
absolute and relative air humidity, wind speed and direction, and data for the base period (1982-2001) at the
selected synoptic station in each province. Correlation and root mean square error (RMSE) coefficients were
used to analyze the performance of the proposed models. These criteria indicated the high accuracy of the
proposed models in downscaling of daily temperature parameter rather than precipitation and wind speed
parameters.

Keywords

Climate

downscaling

Neural network (NN)

Iran