Performance Evaluation of Effective Drought Prediction Using Machine Learning

Abstract

There has been much research recently to improve the prediction of drought, but the frequency and pattern of drought displays an irregular time series that limits its predictability, making it difficult to predict with only a single model, and high-level predictions cannot be made even when many models are applied. Therefore, many studies have been conducted to improve predictions by using explanatory variables such as precipitation, temperature, sunshine duration, and air volume as input data. The purpose of this study is to devise a method for predicting drought using the Standard Precipitation Evaporation Index (SPEI), which represents a complex and difficult time series drought index using climate data for weather phenomena. The Standard Precipitation Evaporation Index is a method of calculating the cumulative precipitation by excluding the cumulative evaporation amount from the cumulative precipitation using precipitation and evapotranspiration data, and the evaporation amount is calculated using the monthly heat index method. The Meteorological Agency evaluated meteorological drought using SPI6, which is a 6-month cumulative precipitation standard, and applied it to machine learning based on monthly data and daily data SPEI6 in this study. As a result, ANN monthly data R2 was 0.488 in Andong and 0.533 in Mungyeong, Gumi 0.594, SVR 0.452, 0.496, 0.564, RF 0.355, 0.467, 0.524, and the daily data are ANN 0.923, 0.919, 0.915, SVR 0.925, 0.923, 0.896, RF 0.915, 0.915, 0.797, and the daily data SPEI at all points. It was confirmed that high prediction was obtained when machine learning was applied to these methods.