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PCA-based multivariate LSTM model for predicting natural groundwater level variations in a time-series record affected by anthropogenic factors

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Abstract

Time series of natural groundwater level considering rainfall effects are usually used for the estimation of groundwater recharge, long-term trend analysis, and assessment of interactions between surface water and groundwater along rivers. However, anthropogenic activities, such as groundwater pumping, land excavation, and barrier construction, may induce abnormal changes in water levels. This study aimed to develop a universal long short-term memory (LSTM) model for predicting natural water level variations in a time-series record that has been affected by groundwater abstraction or other anthropogenic factors. This model uses past and present groundwater levels, rainfall and representative principal components of groundwater level time series as input variables. For this purpose, 17 cases of the developed LSTM model were tested using 13 monitoring wells, of which the case with the highest prediction performance was selected. Among the test cases, case 6 was found to achieve the highest performance, with average RMSE and MAE values of 0.061 and 0.027, respectively. The case 6 model used rainfall, groundwater level of monitoring wells, and four main principal components (1–4) as input variables. Also, its optimum window size was found to be 5. The accuracy of the LSTM model was found to be more strongly affected by window size than by input variables. Although the case 6 LSTM model may have errors for some monitoring wells, it has high potential as a universal model that can consistently determine natural groundwater levels in South Korea. This LSTM model makes it universally available for water level prediction, even with long missing periods in groundwater level time series or in the absence of adjacent observations for model input.

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References

  • Alley WM (2016) Drought-proofing groundwater. Groundwater 54(3):309–309. https://doi.org/10.1111/gwat.12418

    Article  Google Scholar 

  • Cai Z, Ofterdinger U (2016) Analysis of groundwater-level response to rainfall and estimation of annual recharge in fractured hard rock aquifers, NW Ireland. J Hydrol 535:71–84

    Article  Google Scholar 

  • Chung SY, Venkatramanan S, Elzein HE, Selvam S, Prasanna MV (2019) Supplement of missing data in groundwater-level variations of peak type using geostatistical methods. In: Venkatramanan et al (eds) GIS and geostatistical techniques for groundwater science. Elsevier, Amsterdam

    Google Scholar 

  • Cowen CC, Ferguson PA, Jackman DK, Sexauer EA, Vogt C, Woolf HJ (1996) Finding norms of Hadamard multipliers. Linear Algebra Appl 247:217–235

    Article  Google Scholar 

  • Gonçalves RD, Teramoto EH, Engelbrecht BZ, Alfaro Soto MA, Chang HK, van Genuchten MTh (2020) Quasi-saturated layer: implications for estimating recharge and groundwater modeling. Groundwater 58(3):432–440

    Article  Google Scholar 

  • He XH, Ho HV, Lee G, Jung S (2019) Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7):1387. https://doi.org/10.3390/w11071387

    Article  Google Scholar 

  • He L, Chen S, Liang Y, Hou M, Chen J (2020) Infilling the missing values of groundwater level using time and space series: case of Nantong City, east coast of China. Earth Sci Inform 13:1445–1459. https://doi.org/10.1007/s12145-020-00489-y

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  • HRFCO (Han River Flood Control Office) (2021) Water resources management information system (WAMIS). http://www.wamis.go.kr. Accessed 15 Aug 2021

  • Jan CD, Chen TH, Lo WC (2007) Effect of rainfall intensity and distribution of groundwater level fluctuations. J Hydrol 332(3–4):348–360

    Article  Google Scholar 

  • Jiang Y, Guo H, Jia Y, Cao Y, Hu C (2015) Principal component analysis and hierarchical cluster analyses of arsenic groundwater geochemistry in the Hetao basin, Inner Mongolia. Geochemistry 75(2):197–205

    Article  Google Scholar 

  • Jung H, Koh DC, Kim Y, Ha K, Lee J (2019) Interpretation of groundwater level variations in Jeju Island by principal component analysis. Environ Monit Assess 21:1–1

    Article  Google Scholar 

  • Killian CD, Asquith WH, Barlow JRB, Bent GC, Kress WH, Barlow PM, Schmitz DW (2019) Characterizing groundwater and surface-water interaction using hydrograph-separation techniques and groundwater-level data throughout the Mississippi Delta, USA. Hydrogeol J 27:2167–2179

    Article  Google Scholar 

  • Kim GB (2010) Estimation of specific yield using rainfall and groundwater levels at shallow groundwater monitoring sites. J Korea Geo-Environ Soc 11(6):57–67

    Google Scholar 

  • Kim SG, Kim GB (2019) Are groundwater monitoring networks economical? Cost-benefit analysis on the long-term groundwater supply project of South Korea. Water 11:753. https://doi.org/10.3390/w11040753

    Article  Google Scholar 

  • Kim GB, Yum BW (2007) Classification and characterization for water level time series of shallow wells at the national groundwater monitoring stations. J Soil Groundwater Environ 12(5):86–97

    Google Scholar 

  • Kim GB, Son YC, Lee SH, Jeong AC, Cha EJ, Ko MJ (2012) Understanding of surface water-groundwater connectivity in an alluvial plain using statistical methods. J Eng Geol 22(2):207–221

    Article  Google Scholar 

  • Loukika KN, Venkata Reddy K, Durga Rao KHV, Singh A (2020) Estimation of groundwater recharge rate using SWAT MODFLOW model. In: Ghosh J, da Silva I (eds) Applications of geomatics in civil engineering, vol 33. Lecture notes in civil engineering. Springer, Singapore

    Chapter  Google Scholar 

  • MLTMA (Ministry of Land, Transport and Maritime Affairs) (2012) Groundwater management basic plan. Seoul, Korea, 154p (in Korean)

  • MOE (Ministry of Environment) and K-water (2020) National groundwater monitoring network in Korea - Annual report 2020. Daejeon, Korea, 865p (in Korean)

  • MOE (Ministry of Environment) (2019) Annual report of groundwater in South Korea. Daejeon, Korea, 372p (in Korean)

  • Pathak AA, Dodamani BM (2019) Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha river basin, India. Nat Resour Res 28:631–643. https://doi.org/10.1007/s11053-018-9417-0

    Article  Google Scholar 

  • Rama F, Miotlinski K, Franco D, Corseuil HX (2018) Recharge estimation from discrete water-table datasets in a coastal shallow aquifer in a humid subtropical climate. Hydrogeol J 26:1887–1902

    Article  Google Scholar 

  • Ravikumar P, Somashekar RK (2017) Principal component analysis and hydrochemical facies characterization to evaluate groundwater quality in Varahi river basin, Karnataka state, India. Appl Water Sci 7:745–755

    Article  Google Scholar 

  • Seferli S, Modis K, Adam K (2019) Interpretation of groundwater hydrographs in the West Thessaly basin, Greece, using principal component analysis. Environ Earth Sci 78:257. https://doi.org/10.1007/s12665-019-8262-8

    Article  Google Scholar 

  • Sun X, Bernard-Jannin L, Garneau C, Volk M, Arnold JG, Srinivasan R, Sauvage S, Sanchez-Perez JM (2016) Improved simulation of river water and groundwater exchange in an alluvial plain using the SWAT model. Hydrol Process 30(2):187–202

    Article  Google Scholar 

  • Takafuji EHd, Rocha MM, Manzione RL (2019) Groundwater level prediction/forecasting and assessment of uncertainty using SGS and ARIMA models: a case study in the Bauru Aquifer System (Brazil). Nat Resour Res 28:487–503. https://doi.org/10.1007/s11053-018-9403-6

    Article  Google Scholar 

  • Taylor CJ, Alley WM (2001) Ground water level monitoring and the importance of long-term water level data. US Geological Survey Circular 1217, US Department of the Interior, Denver

  • Vu MT, Jardani A, Massei N, Fournier M (2021) Reconstruction of missing groundwater level data by using long short-term memory (LSTM) deep neural network. J Hydrol 597:125776. https://doi.org/10.1016/j.jhydrol.2020.125776

    Article  Google Scholar 

  • Wang W, Zhang Z, Duan L, Wang Z, Zhao Y, Zhang Q, Dai M, Liu H, Zheng X, Sun Y (2018) Response of the groundwater system in the Guanzhong Basin (central China) to climate change and human activities. Hydrogeol J 26:1429–1441. https://doi.org/10.1007/s10040-018-1757-7

    Article  Google Scholar 

  • Winter TC, Mallory SE, Allen TR, Rosenberry DO (2000) The use of principal component analysis for interpreting ground water hydrographs. Groundwater 38(2):234–246

    Article  Google Scholar 

  • Yi MJ, Kim GB, Sohn YC, Lee JY, Lee KK (2004) Time series analysis of groundwater level data obtained from national groundwater monitoring stations. J Geol Soc Korea 40(3):305–329

    Google Scholar 

  • Yoon H, Hyun Y, Ha K, Lee KK, Kim GB (2016) A method to improve the stability and accuracy of ANN and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 90:144–155

    Article  Google Scholar 

  • Yoon H, Kim Y, Ha K, Lee SH, Kim GP (2017) Comparative evaluation of ANN- and SVM-time series models for predicting freshwater-saltwater interface fluctuations. Water 9(5):323. https://doi.org/10.36390/w9050323

    Article  Google Scholar 

  • Zamanirad M, Sedghi H, Sarraf A, Saremi A, Rezaee P (2018) Potential impacts of climate change on groundwater levels on the Kerdi-Shirazi plain, Iran. Environ Earth Sci 77:415. https://doi.org/10.1007/s12665-018-7585-1

    Article  Google Scholar 

  • Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929

    Article  Google Scholar 

  • Zhu M, Wang S, Kong X, Zheng W, Feng W, Zhang X, Yuan R, Song X, Sprenger M (2019) Interaction of surface water and groundwater influenced by groundwater over-extraction, waste water discharge and water transfer in Xiong’an new area. China Water 11:539

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Research Foundation of Korea and the Ministry of Science and ICT (No. NRF-2019R1A2C1088085).

Funding

This research is funded by the National Research Foundation of Korea.

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Authors and Affiliations

  1. Department of Construction Safety and Disaster Prevention, Daejeon University, Daejeon City, Republic of Korea

    Gyoo-Bum Kim

  2. Industry-Academic Cooperation Foundation, Daejeon University, Daejeon City, Republic of Korea

    Chan-Ik Hwang

  3. Department of Disaster Prevention, Graduate School of Daejeon University, Daejeon City, Republic of Korea

    Myoung-Rak Choi

Authors
  1. Gyoo-Bum Kim
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  2. Chan-Ik Hwang
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  3. Myoung-Rak Choi
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Contributions

Data curation, M-RC; formal analysis, C-IH; funding acquisition, G-BK; investigation, G-BK; methodology, C-IH; project administration, G-BK; resources, G-BK; supervision, G-BK; validation, G-BK; writing—original draft, G-BK; and writing—review and editing, G-BK.

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Correspondence to Gyoo-Bum Kim.

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Kim, GB., Hwang, CI. & Choi, MR. PCA-based multivariate LSTM model for predicting natural groundwater level variations in a time-series record affected by anthropogenic factors. Environ Earth Sci 80, 657 (2021). https://doi.org/10.1007/s12665-021-09957-0

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