Application of Random Forest for Identification of an Appropriate Model for Predicting Meteorological Drought.

Application of Random Forest for Identification of an Appropriate Model for Predicting Meteorological Drought.



This research aims to find the best model for predicting the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) in the future. The study estimates SPI and SPEI at different time scales, ranging from 1 to 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months for the estimated drought indices (SPI and SPEI). Accuracy and error metrics like Nash–Sutcliffe efficiency (NSE), root-mean-square error (RMSE), producer accuracy (PA), user accuracy (UA), and Choen’s kappa are used to assess the models. Read the research Paper!

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