Journal Article

Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network

Shin, Mun-Ju, Kang, Kyung Goo, Moon, Duk-Chul, Moon, Soo-Hyoung, Koh, Hyuk-Joon

2020

Overview

To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction performance of LSTM was remarkably high. If the groundwater level is simulated on the assumption that the future withdrawal amount is reduced by 1/3 of the current groundwater withdrawal, the range of the maximum rise of the groundwater level would be 0.06–0.13 m compared to the current condition. In addition, assuming that no groundwater is taken, the range of the maximum increase in the groundwater level would be 0.11–0.38 m more than the current condition. Therefore, the effect of groundwater withdrawal on the groundwater level in this area was exceedingly small. The method and results can be used to develop new groundwater withdrawal sources for the redistribution of groundwater withdrawals.

• Publisher: MDPI AG
• Subject: Deep learning / Environmental aspects / Groundwater / groundwater extraction / groundwater level prediction / groundwater level variation / Groundwater levels / groundwater withdrawal impact / Hydrology / Impact analysis / Long Short-Term Memory / Machine learning / Neural networks / Performance prediction / Precipitation / prediction / Rain / South Korea / Support vector machines / Water levels / Water resources / water table