Khedmatkar Bolandakhtar, Mohammad and Golian, Saeed (2019) Determining the best combination of MODIS data as input to ANN models for simulation of rainfall. Theoretical and Applied Climatology, 138. pp. 1323-1332. ISSN 0177-798X

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Abstract

In recent years, satellite data has been used to estimate precipitation with the aim of increasing the accuracy of rainfall spatial distribution especially at ungauged locations. In this research, the satellite data, including visible and infrared reflection data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and observation data, consists of rainfall records (10 years 2005–2015) from three synoptic stations in Semnan province, were used to simulate rainfall using an artificial neural network (ANN) method. The network performance is evaluated through three performance criteria, i.e., correlation coefficient (R), root mean square error (RMSE), and Nash–Sutcliffe (NS). Findings show that using a combination of visible reflection data of band 3 and infrared reelection data of bands 5, 18, and 19 as input data results in better performance compared with other possible combinations. In this model, the values of R, NS, and RMSE for test period data were 0.93, 0.81, and 1.49, respectively.

Item Type:	Article
Additional Information:	Cite as: Bolandakhtar, M.K., Golian, S. Determining the best combination of MODIS data as input to ANN models for simulation of rainfall. Theor Appl Climatol 138, 1323–1332 (2019). https://doi.org/10.1007/s00704-019-02884-y
Keywords:	Determining; best combination; MODIS data; input; ANN models; simulation of rainfall;
Academic Unit:	Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	13662
Identification Number:	https://doi.org/10.1007/s00704-019-02884-y
Depositing User:	Saeed Golian
Date Deposited:	25 Nov 2020 16:08
Journal or Publication Title:	Theoretical and Applied Climatology
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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