Impact of using additional precipitation data from the uppermost region on improving the performance of AI models in predicting groundwater levels

Groundwater is an important water resource that is widely used worldwide for agricultural, industrial, and domestic purposes. In the case of Jeju Island, located in southern South Korea, groundwater is an indispensable water resource that accounts for 82% of the total water supply. Therefore, scientific prediction and management of groundwater levels are very important for the sustainable use of groundwater by citizens. This study additionally used precipitation data from the Baekrokdam Climate Change Observatory located on the summit of Jeju Island in artificial intelligence (AI) models to accurately predict one-month-ahead future groundwater levels for the mid-mountainous areas of Jeju Island, where groundwater levels are highly variable. In other words, the AI models compared and analyzed the improvement effect of the monthly groundwater level prediction performance for 1) using precipitation data from two rainfall stations, groundwater withdrawal data from two groundwater sources, and groundwater level data from two monitoring wells in the study area, and 2) adding precipitation data from Baekrokdam Climate Change Observatory. The study subjects are two groundwater level monitoring wells located at 435-471m above mean sea level in the southeast of Jeju Island. The AI models used to predict groundwater levels are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), a deep learning AI model.

As a result, when the Baekrokdam precipitation data were not used, the two AI models showed excellent groundwater level prediction performance with Nash-Sutcliffe efficiency (NSE) values of 0.871 or higher. The LSTM model showed relatively higher prediction performance for high and low groundwater levels than the ANN model. This means that the LSTM model adequately incorporates the seasonal effects of wet and dry periods into groundwater level simulations. The more volatile the observed groundwater level, the more difficult it is for the AI models to interpret the characteristics of groundwater level fluctuations, and the lower the performance of predicting future groundwater levels. When additional Baekrokdam precipitation data were used, the two AI models showed improved groundwater level prediction performance by having NSE values of 0.907 or higher. This means that the additional use of precipitation data located in the uppermost region provides more information to help interpret groundwater levels, allowing AI models to better interpret the characteristics of groundwater level fluctuations. In addition, the use of Baekrokdam precipitation data was more helpful in improving groundwater level prediction for the monitoring well, which has highly variable groundwater levels that are difficult to predict, and the ANN model with relatively low groundwater level prediction performance. When additional Baekrokdam precipitation data was used for a specific monitoring well, the groundwater level prediction performance of the ANN model was improved to a level comparable to that of the LSTM model, which is a deep learning AI, even with a relatively simple ANN model structure. This is an example of how important it is to use additional useful data in research using AI models.