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

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

doi:10.3390/hydrology7030064

Cited by 31 publications

(9 citation statements)

References 32 publications

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“…Long short-term memory network (LSTM) is a special kind of RNN, which is capable of learning long-term dependencies. LSTM was first introduced in 1997 40 and was refined and popularized by many researchers 26 , 41 . LSTM works well on a large variety of problems and is now popularly used in water level prediction and forecasting, stock market forecasting etc 26 , 42 .…”

Section: Methodsmentioning

confidence: 99%

An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

Raihan

Bauer

Mukhopadhaya³

2022

Sci Rep

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Uckermark, a district of the state Brandenburg, Germany is situated in one of the driest regions of Germany. The district is known for its agricultural activities and natural resources. But in recent times the district is being prone to groundwater deficit due to the dryness of its climate. In this research initiative, a GIS and Remote Sensing based approach has been made to detect the potential groundwater recharge zones of Uckermark district and observe the groundwater level condition over a period of 21 years (2000–2020). Analytic Hierarchy Process has been used to locate the potential groundwater recharge zones and later a Long Short-Term Memory (LSTM) based model has been developed to forecast the seasonal groundwater level for the upcoming five years in the potential groundwater recharge zones based on observation data from groundwater measurement points. This enabled us to see the groundwater condition of Uckermark in near future and point out the necessary steps to be taken.

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Section: Methodsmentioning

confidence: 99%

An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

Raihan

Bauer

Mukhopadhaya³

2022

Sci Rep

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“…대부분의 섬 지역은 생활 및 농업용수의 80% 이상을 지하수에 의존하는 등 지하수를 주요 수자원으로 활용한 다 (Shin et al, 2020). 제주도는 우리나라의 대표적 섬 지 역이며 해당 지역에서 이용되는 담수의 92% 이상을 지 하수에 의존하고 있다 (Yang, 2007) (Go, 2006 (Go et al, 2006;Kim, 2018) (Choi, 1992;Hahn et al, 1997;Kim et al, 2006;Won et al, 2005;Won et al, 2006;El-Kadi et al, 2013 (Ok, 2010;Kim, 2011, Lee, 2008…”

Section: 서 론unclassified

Data-driven Analysis for Developing the Effective Groundwater Management System in Daejeong-Hangyeong Watershed in Jeju Island

Lee

Jeong

Kim³

et al. 2021

Econ. Environ. Geol.

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In this study, the impact of clustered groundwater usage facilities and the proper amount of groundwater usage in the Daejeong-Hangyeong watershed of Jeju island were evaluated based on the data-driven analysis methods. As the applied data, groundwater level data; the corresponding precipitation data; the groundwater usage amount data (Jeoji, Geumak, Seogwang, and English-education city facilities) were used. The results show that the Geumak usage facility has a large influence centering on the corresponding location; the Seogwang usage facility affects on the downstream area; the English-education usage facility has a great impact around the upstream of the location; the Jeoji usage facility shows an influence around the up-and down-streams of the location. Overall, the influence of operating the clustered groundwater usage facilities in the watershed is prolonged to approximately 5km. Additionally, the appropriate groundwater usage amount to maintain the groundwater base-level was analyzed corresponding to the precipitation. Considering the recent precipitation pattern, there is a need to limit the current amount of groundwater usage to 80%. With increasing the precipitation by 100mm, additional groundwater development of approximately 1,500m 3 -1,900m 3 would be reasonable. All the results of the developed data-driven estimation model can be used as useful information for sustainable groundwater development in the Daejeong-Hangyeong watershed of Jeju island.

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“…Recent studies have used LSTM models [29][30][31][32][33][34][35], ML models [36][37][38][39] and ANN models [40,41] to forecast groundwater level (GWL). Earlier investigations have forecasted the GWL within the time series of observed data while others have forecasted for three months [36].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Modeling of Groundwater Storage Change

Haq¹,

Jilani²,

Prabu³

2022

Computers, Materials &Amp; Continua

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The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003-2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from −5.88 ± 1.2 mm/year to −14.12 ± 1.2 mm/year and −3.5 ± 1.5 to −10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from −7.78 ± 1.2 to −15.6 ± 1.2 for TWSC and −4.97 ± 1.5 to −12.21 ± 1.5 for GWSC from 2020-2025. An interesting observation was a minor increase in rainfall during the study period for three basins.

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

Cited by 31 publications

References 32 publications

An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany

Data-driven Analysis for Developing the Effective Groundwater Management System in Daejeong-Hangyeong Watershed in Jeju Island

Deep Learning Based Modeling of Groundwater Storage Change

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