DIFFERENCE WATER INDEX
; SOIL-MOISTURE
; TEMPERATURE/VEGETATION INDEX
; CHALLENGES
; SYSTEM
; MODEL
; NDWI
WOS学科分类:
Water Resources
WOS研究方向:
Water Resources
英文摘要:
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, R-2 = 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, R-2 = 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.
1.Seoul Natl Univ, Interdisciplinary Program Landscape Architecture, Seoul 08826, South Korea 2.Seoul Natl Univ, Grad Sch Environm Studies, Dept Environm Planning, Seoul 08826, South Korea 3.Seoul Natl Univ, Res Inst Agr Life Sci, Dept Landscape Architecture & Rural Syst Engn, Seoul 08826, South Korea
Recommended Citation:
Park, Haekyung,Kim, Kyungmin,Lee, Dong Kun. Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data[J]. WATER,2019-01-01,11(4)