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http://dx.doi.org/10.3741/JKWRA.2020.53.4.273

Evaluating the groundwater prediction using LSTM model  

Park, Changhui (R&D Center, GeoGreen21)
Chung, Il-Moon (Department of Land, Water and Environment Research, KICT)
Publication Information
Journal of Korea Water Resources Association / v.53, no.4, 2020 , pp. 273-283 More about this Journal
Abstract
Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R2) was 0.98 on average, indicating that the predictive model developed was very accurate.
Keywords
Groundwater level; Prediction; Jeju Island; Long short term memory; ANN;
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Times Cited By KSCI : 2  (Citation Analysis)
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