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DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA
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| Zeitschriftentitel: | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , , |
| In: | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 2016, S. 157-158 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Copernicus GmbH
|
| author_facet |
Rhee, J. Im, J. Park, S. Rhee, J. Im, J. Park, S. |
|---|---|
| author |
Rhee, J. Im, J. Park, S. |
| spellingShingle |
Rhee, J. Im, J. Park, S. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| author_sort |
rhee, j. |
| spelling |
Rhee, J. Im, J. Park, S. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprsarchives-xli-b8-157-2016 <jats:p>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.</jats:p> DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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10.5194/isprsarchives-xli-b8-157-2016 |
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Copernicus GmbH, 2016 |
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Copernicus GmbH, 2016 |
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ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| title |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_unstemmed |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_full |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_fullStr |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_full_unstemmed |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_short |
DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_sort |
drought forecasting based on machine learning of remote sensing and long-range forecast data |
| url |
http://dx.doi.org/10.5194/isprsarchives-xli-b8-157-2016 |
| publishDate |
2016 |
| physical |
157-158 |
| description |
<jats:p>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.</jats:p> |
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| author | Rhee, J., Im, J., Park, S. |
| author_facet | Rhee, J., Im, J., Park, S., Rhee, J., Im, J., Park, S. |
| author_sort | rhee, j. |
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| container_title | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| description | <jats:p>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.</jats:p> |
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| imprint | Copernicus GmbH, 2016 |
| imprint_str_mv | Copernicus GmbH, 2016 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 2194-9034 |
| issn_str_mv | 2194-9034 |
| language | English |
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| physical | 157-158 |
| publishDate | 2016 |
| publishDateSort | 2016 |
| publisher | Copernicus GmbH |
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| series | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| source_id | 49 |
| spelling | Rhee, J. Im, J. Park, S. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprsarchives-xli-b8-157-2016 <jats:p>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.</jats:p> DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Rhee, J., Im, J., Park, S., ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_full | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_fullStr | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_full_unstemmed | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_short | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| title_sort | drought forecasting based on machine learning of remote sensing and long-range forecast data |
| title_unstemmed | DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA |
| url | http://dx.doi.org/10.5194/isprsarchives-xli-b8-157-2016 |