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Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 1323–1332 | Cite as

Determining the best combination of MODIS data as input to ANN models for simulation of rainfall

  • Mohammad Khedmatkar Bolandakhtar
  • Saeed GolianEmail author
Original Paper

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.

Notes

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Khedmatkar Bolandakhtar
    • 1
  • Saeed Golian
    • 1
    Email author
  1. 1.Department of Civil EngineeringShahrood University of TechnologyShahroudIran

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