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Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data by Haekyung Park; Kyungmin Kim; Dong kun Lee is a Environmental Science article available to read on EtoBox.

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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 R

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Author

Haekyung Park; Kyungmin Kim; Dong kun Lee

Publisher

MDPI AG

Published

2019

Language

EN

Field

Environmental Science (Physical Sciences)