Skip to main content
SpringerLink
Log in

On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Management of groundwater resources needs continuous and efficient monitoring networks. Sparsity of in situ measurements both spatially and temporally creates hindrance in framing groundwater management policies. Remotely sensed data can be a possible alternative. GRACE satellites can trace groundwater changes globally. Moreover, gridded rainfall (RF) and soil moisture (SM) data can shed some light on the hydrologic system. The present study attempts to use GRACE, RF and SM data at a local scale to predict groundwater level. Ground referencing of satellite data were done by using three machine learning techniques- Support Vector Regression (SVR), Random Forest Method (RFM) and Gradient Boosting Mechanism (GBM). The performance of the developed methodology was tested on a part of the Indo-Gangetic basin. The analyses were carried out for nine GRACE pixels to identify relationship between individual well measurements and satellite-derived data. These nine pixels are classified on the basis of presence or absence of hydrological features. Pixels with the presence of perennial streams showed reasonably good results. However, pixels with wells located mostly near the stream gave relatively poorer predictions. These results help in identifying wells which can reasonably represent the regional shallow groundwater dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data: a machine learning approach. Adv Water Resour 33(1):69–80. https://doi.org/10.1016/j.advwatres.2009.10.008

    Article  Google Scholar 

  • Awange JL, Gebremichael M, Forootan E, Wakbulcho G, Anyah R, Ferreira VG, Alemayehu T (2014) Characterization of Ethiopian mega hydrogeological regimes using GRACE, TRMM and GLDAS datasets. Adv Water Resour 74:64–78. https://doi.org/10.1016/j.advwatres.2014.07.012

    Article  Google Scholar 

  • Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Proces Lett Rev 11(10):203–224

    Google Scholar 

  • Bell B, Wallace B, Zhang D (2012) Forecasting river runoff through support vector machines. In Sugawara K, Wang Y, Hattori F, Nishida T, Fujita S, Kinsner W, Zadeh LA (eds) Proc. 11th IEEE Int. Conf. on Cognitive Informatics Cognitive Computing (ICCI*CC’12). IEEE, pp 58–64

  • Bertoldi G, Della Chiesa S, Notarnicola C, Pasolli L, Niedrist G, Tappeiner U (2014) Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling. J Hydrol 516:245–257. https://doi.org/10.1016/j.jhydrol.2014.02.018

    Article  Google Scholar 

  • Bhanja SN, Mukherjee A, Saha D, Velicogna I, Famiglietti JS (2016) Validation of GRACE based groundwater storage anomaly using in-situ groundwater level measurements in India. J Hydrol 543:729–738. https://doi.org/10.1016/j.jhydrol.2016.10.042

    Article  Google Scholar 

  • Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Jones Z, Casalicchio G (2016) Mlr: Machine Learning in R. R package version 2.9. https://CRAN.R-project.org/package=mlr

  • Bray M, Han D (2004) Identification of support vector machines for runoff modeling. J Hydroinf 6(4):265–280

    Google Scholar 

  • Breiman LEO (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Carreiras JMB, Melo JB, Vasconcelos MJ (2013) Estimating the above-ground biomass in Miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sens 5(1524–1548):2013

    Google Scholar 

  • Central Water Commission (CWC) (2014) Ganga Basin Report. Central Water Commission, Ministry of Water Resources, Government of India

    Google Scholar 

  • Chen L, Yeh K, Wei H, Liu G (2011) An improved genetic programming to SSM/I estimation typhoon precipitation over ocean. Hydrol Process 25(16):2573–2583. https://doi.org/10.1002/hyp.8132

    Article  Google Scholar 

  • Dube T, Mutanga O, Abdel-Rahman EM, Ismail R, Slotow R (2015) Predicting eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multisource data sets. Int J Remote Sens 36(14):3751–3772. https://doi.org/10.1080/01431161.2015.1070316

    Article  Google Scholar 

  • Erdal HI, Karakurt O (2013) Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. J Hydrol 477:119–128. https://doi.org/10.1016/j.jhydrol.2012.11.015

    Article  Google Scholar 

  • Famiglietti JS (2014) The global groundwater crisis. Nat Clim Chang 4(11):945–948. https://doi.org/10.1038/nclimate2425

    Article  Google Scholar 

  • Fan Y, van den Dool H (2004) Climate prediction center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J Geophys Res D: Atmos 109(10):1–8

    Google Scholar 

  • Forootan E, Rietbroek R, Kusche J, Sharifi MA, Awange JL, Schmidt M, Omondi P, Famiglietti J (2014) Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sens Environ 140:580–595. https://doi.org/10.1016/j.rse.2013.09.025

    Article  Google Scholar 

  • Frappart F, Seoane L, Ramillien G (2013) Validation of GRACE-derived terrestrial water storage from a regional approach over South America. Remote Sens Environ 137:69–83. https://doi.org/10.1016/j.rse.2013.06.008

    Article  Google Scholar 

  • Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451

    Article  Google Scholar 

  • Grömping U (2006) Relative importance for linear regression in R: the package relaimpo. J Stat Softw 17(1):1–27

    Article  Google Scholar 

  • Guelman L (2012) Expert systems with applications gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst Appl 39(3):3659–3667. https://doi.org/10.1016/j.eswa.2011.09.058

    Article  Google Scholar 

  • Huang J, Halpenny J, van der Wal W, Klatt C, James TS, Rivera A (2012) Detectability of groundwater storage change within the Great Lakes Water Basin using GRACE. J Geophys Res 117(B8):B08401

    Article  Google Scholar 

  • Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) Kernlab - an S4 package for kernel methods in R. J Stat Softw 11(9):1–20 URL http://www.jstatsoft.org/v11/i09/

    Article  Google Scholar 

  • Li PH, Kwon H-H, Sun L, Lall U, Kaoa JJ (2010) A modified support vector machine based prediction model on streamflow at the Shihmen reservoir, Taiwan. Int J Climatol 30:1256–1268

    Article  Google Scholar 

  • Li B, Yang G, Wan R, Dai X, Zhang Y (2016) Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang lake in China. Hydrol Res:1–13

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

  • Liesch T, Ohmer M (2016) Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in Jordan. Hydrogeol J 24(6):1547–1563. https://doi.org/10.1007/s10040-016-1416-9

    Article  Google Scholar 

  • Liu, D., Yu, Z-b., and, Hai-shen, L. (2010). Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction. Water Sci Eng, 3(4):361–377

  • Long D, Scanlon BR, Longuevergne L, Sun AY, Fernando DN, Save H (2013) GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophys Res Lett 40(13):3395–3401. https://doi.org/10.1002/grl.50655

    Article  Google Scholar 

  • Makkeasorn A, Chang NB, Li J (2009) Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. J Environ Manag 90(2):1069–1080. https://doi.org/10.1016/j.jenvman.2008.04.004

    Article  Google Scholar 

  • Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2015) e1071: Misc functions of the Department of Statistics, probability theory group (formerly: E1071), TU Wien. R Packag Version 1:6–7 https://CRAN.R-project.org/package=e1071

    Google Scholar 

  • Moore S, Fisher JB (2012) Challenges and opportunities in GRACE-based groundwater storage assessment and management: an example from Yemen. Water Resour Manag 26(6):1425–1453. https://doi.org/10.1007/s11269-011-9966-z

    Article  Google Scholar 

  • Moritz S (2016) ImputeTS: time series missing value imputation. R package version 1.8, https://CRAN.R-project.org/package=imputeTS

  • Morrow E, Mitrovica JX, Fotopoulos G (2011) Water storage, net precipitation, and evapotranspiration in the Mackenzie River basin from October 2002 to September 2009 inferred from GRACE satellite gravity data. J Hydrometeorol 12(3):467–473. https://doi.org/10.1175/2010JHM1278.1

    Article  Google Scholar 

  • Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resour Manag 31(9):2761–2775. https://doi.org/10.1007/s11269-017-1660-3

    Article  Google Scholar 

  • Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobotics 7:1–21

    Article  Google Scholar 

  • Pai P-F, Lin K-P, Lin C-S, Chang P-T (2010) Time series forecasting by a seasonal support vector regression model. Expert Syst Appl 37(6):4261–4265. https://doi.org/10.1016/j.eswa.2009.11.076

    Article  Google Scholar 

  • R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://www.R-project.org/

    Google Scholar 

  • Ridgeway, G. with contributions from others (2015). gbm: Generalized Boosted Regression Models. R package version 2.1.1. https://CRAN.R-project.org/package=gbm

  • Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive modeling of groundwater nitrate pollution using random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (southern Spain). Sci Total Environ 476-477:189–206. https://doi.org/10.1016/j.scitotenv.2014.01.001

    Article  Google Scholar 

  • Scanlon BR, Longuevergne L, Long D (2012) Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA. Water Resour Res 48(4):1–9

    Article  Google Scholar 

  • Shamsudduha M, Taylor RG, Longuevergne L (2012) Monitoring groundwater storage changes in the highly seasonal humid tropics: validation of GRACE measurements in the Bengal Basin. Water Resour Res 48(2):1–12

    Article  Google Scholar 

  • Shortridge JE, Guikema SD, Benjamin F, Zaitchik BF (2016) Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrol Earth Sci 20(7):2611–2628. https://doi.org/10.5194/hess-20-2611-2016

    Article  Google Scholar 

  • Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88

    Article  Google Scholar 

  • Smola A, Vishwanathan SVN (2008) Introduction to machine learning. Cambridge University Press, Cambridge

    Google Scholar 

  • Soni A, Syed TH (2015) Diagnosing land water storage variations in Major Indian River basins using GRACE observations. Glob Planet Chang 133:263–271. https://doi.org/10.1016/j.gloplacha.2015.09.007

    Article  Google Scholar 

  • Strassberg G, Scanlon BR, Rodell M (2007) Comparison of seasonal terrestrial water storage variations from GRACE with groundwater-level measurements from the High Plains aquifer (USA). Geophys Res Lett 34:1–5

    Article  Google Scholar 

  • Sun AY (2013) Predicting groundwater level changes using GRACE data. Water Resour Res 49(5900):5900–5912. https://doi.org/10.1002/wrcr.20421

    Article  Google Scholar 

  • Sun AY, Green R, Swenson S, Rodell M (2012) Toward calibration of regional groundwater models using GRACE data. J Hydrol 422–423:1–9

    Article  Google Scholar 

  • Swenson S, Wahr J (2006) Estimating large-scale precipitation minus evapotranspiration from GRACE satellite gravity measurements. J Hydrometeorol 7(2):252–270. https://doi.org/10.1175/JHM478.1

    Article  Google Scholar 

  • Vapnik VN (2013) The nature of statistical learning theory. Springer, Berlin

    Google Scholar 

  • Vitorino D, Coelho ST, Santos P, Sheets S, Jurkovac B, Amado C (2014). A Random Forest Algorithm applied to condition-based wastewater deterioration modeling and forecasting. In Procedia Engineering, volume 89, pages 401–410. 16th Conference on Water Distribution System Analysis, WDSA 2014, Elsevier

  • Wada Y, Van Beek LPH, Van Kempen CM, Reckman JWTM, Vasak S, Bierkens MFP (2010) Global depletion of groundwater resources. Geophys Res Lett 37(20):1–5

    Article  Google Scholar 

  • Yeh PJ, Swenson SC, Famiglietti JS, Rodell M (2006) Remote sensing of groundwater storage changes in Illinois using the gravity recovery and climate experiment ( GRACE). Water Resour Res 42:1–7

    Article  Google Scholar 

  • Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716. https://doi.org/10.1016/j.jhydrol.2006.01.021

    Article  Google Scholar 

Download references

Acknowledgements

Authors are thankful to Central Ground Water Board (CGWB), INDIA for providing necessary data for this work. Open-source data of CGWB (India-WRIS), Ministry of Water Resources was used in the present study.

Author information

Authors and Affiliations

  1. School of Water Resources, Indian Institute of Technology Kharagpur, WB721302, Kharagpur, India

    Madhumita Sahoo

  2. Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Aman Kasot & Anirban Dhar

  3. Central Ground Water Board, Bhujal Bhawan, Faridabad, Haryana, India

    Amlanjyoti Kar

Authors
  1. Madhumita Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aman Kasot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anirban Dhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amlanjyoti Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madhumita Sahoo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahoo, M., Kasot, A., Dhar, A. et al. On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations. Water Resour Manage 32, 1225–1244 (2018). https://doi.org/10.1007/s11269-017-1865-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-017-1865-5

Keywords

Search

Navigation