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Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

1
Interdisciplinary Program in Landscape Architecture, Seoul National University, Seoul 08826, Korea
2
Department of Environmental Planning, Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea
3
Department of Landscape Architecture and Rural System Engineering, Research Institute of Agriculture Life Sciences, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 705; https://doi.org/10.3390/w11040705
Received: 6 March 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 5 April 2019
(This article belongs to the Section Water Resources Management and Governance)
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Abstract

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage. View Full-Text
Keywords: agricultural drought; prediction; machine learning; random forest; soil moisture; climate change mitigation; Landsat-8 agricultural drought; prediction; machine learning; random forest; soil moisture; climate change mitigation; Landsat-8
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Park, H.; Kim, K.; Lee, D. Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data. Water 2019, 11, 705.

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