The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 157–158, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-157-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 157–158, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-157-2016

  22 Jun 2016

22 Jun 2016

DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

J. Rhee1, J. Im2, and S. Park2 J. Rhee et al.
  • 1APEC Climate Centre, Climate Research Department, 12 Centum 7-ro, Haeundae-gu, Busan, 48058, South Korea
  • 2School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, South Korea

Keywords: Drought, Forecasting, Machine learning, Long-range forecast, Remote sensing data

Abstract. The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.