Author(s): Lee; Joo-Heon; Jang; Ho-Won; Seo; Gil-Su; Baek; Seul-Gi
Linked Author(s):
Keywords: Rtificial Neural Network; Drought; Multilayer perceptron; SPI; Forecasting
Abstract: In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with drought-forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study attempts to forecast drought in Korea by using an Artificial Neural Network Model and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. Standardized Precipitation Index (SPI) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the Multi Layer Perceptron (MLP) Neural Network model. Data set from 1976to 2000 was selected as the training period for the parameter calibration, and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6), and SPI (12) over different forecasting lead times (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of a 1-month forecast lead time. Meanwhile, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time, and SPI (12) shows relatively good results in case of up to 1-5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only a 1-month short-term drought forecast. SPI (6) and SPI (12) have an advantage over long-term drought forecast for 3-5 months lead time.
Year: 2014