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http://dx.doi.org/10.3741/JKWRA.2020.53.2.131

Possibility analysisof future droughts using long short term memory and standardized groundwater level index  

Lim, Jae Deok (School of Civil and Environmental Engineering, Kookmin University, College of Creative Engineering)
Yang, Jeong-Seok (School of Civil and Environmental Engineering, Kookmin University, College of Creative Engineering)
Publication Information
Journal of Korea Water Resources Association / v.53, no.2, 2020 , pp. 131-140 More about this Journal
Abstract
The purpose of this study is to analyze the possibility of future droughts by calculating the Standardized Groundwater level Index(SGI) after predicting groundwater level using Long Short Term Memory (LSTM) model. The groundwater level of the Kumho River basin was predicted for the next three years by using the LSTM model, and it was validated through RMSE after learning with observation data except the last three years. The temporal SGI was calculated by using the prediction data and the observation data. The calculated SGI was interpolated within the study area, and the spatial SGI was calculated as the average value for each catchment using the interpolated SGI. The possibility of spatio-temporal drought was analyzed using calculated spatio-temporal SGI. It is confirmed that there is a spatio-temporal difference in the possibility of drought. Through the improvement of deep learning model and diversification of validation method, it is expected to obtain more reliable prediction results and the expansion of study area can be used to respond to drought nationwide, and furthermore it can provide important information for future water resource management.
Keywords
Deep learning; Long short term memory (LSTM); Standardized groundwater level index (SGI); Drought; Kumho river;
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Times Cited By KSCI : 8  (Citation Analysis)
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