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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 368
Proc. IAHS, 368, 46–50, 2015
https://doi.org/10.5194/piahs-368-46-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 368, 46–50, 2015
https://doi.org/10.5194/piahs-368-46-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2015

06 May 2015

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

H. Komeilian2, S. Mohyeddin Bateni3, T. Xu1, and J. Nielson3 H. Komeilian et al.
  • 1State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, 100875, China
  • 2Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran Province, 11369, Iran
  • 3Department of Civil and Environmental Engineering and Water Resource Research Center, University of Hawaii at Manoa, Honolulu, HI, 96822, USA

Keywords: Atmospheric visibility, MODIS data, back-propagation artificial neural network, supporting vector regression

Abstract. Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009–2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson’s correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

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