Advertisement

Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

  • Pragnaditya Malakar
  • Abhijit MukherjeeEmail author
  • Sudeshna Sarkar
Chapter
Part of the Springer Hydrogeology book series (SPRINGERHYDRO)

Abstract

It is a challenging task now and it will be even more challenging in the future to provide an adequate amount of water to the people in this planet. A proper understanding of present day and future water resources is a serious issue, where worldwide increase in the population further promotes water scarcity problem. Groundwater is the major source of freshwater in most parts of South Asia. Some parts are becoming extensively vulnerable as the consumption groundwater in those areas is prominently faster than restored naturally. Application of artificial intelligence (AI) and data mining techniques can be a big help to determine the influence and interdependence of controlling parameters to delineate future groundwater trends and resources. Use of data mining and AI with available spatio-temporal data (satellite and field-based measurements) of governing factors with the help of high-performance computing, and scalable algorithms would help to explore such complex influences and interferences on the groundwater resource dynamics. The outcome would be to predict future trends of available groundwater in changing socio-economic scenarios.

Keywords

Groundwater resources Prediction Artificial intelligence South Asia 

References

  1. Banerjee P, Prasad RK, Singh VS (2009) Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol 58(6):1239–1246.  https://doi.org/10.1007/s00254-008-1619-zCrossRefGoogle Scholar
  2. Banerjee P, Singh VS, Chatttopadhyay K, Chandra PC, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3–4):212–220.  https://doi.org/10.1016/j.jhydrol.2010.12.016CrossRefGoogle Scholar
  3. Chattopadhyay M, Chattopadhyay S (2016) Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall. Theoret Appl Climatol 126(1–2):93–104.  https://doi.org/10.1007/s00704-015-1544-5CrossRefGoogle Scholar
  4. Coppola E, Szidarovszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. J Hydrol Eng 8(6):348–360.  https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348)CrossRefGoogle Scholar
  5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297.  https://doi.org/10.1023/A:1022627411411CrossRefGoogle Scholar
  6. Dash NB, Panda SN, Remesan R, Sahoo N (2010) Hybrid neural modeling for groundwater level prediction. Neural Comput Appl 19(8):1251–1263.  https://doi.org/10.1007/s00521-010-0360-1CrossRefGoogle Scholar
  7. Dhanya CT, Kumar DN (2009a) Data mining for evolution of association rules for droughts and floods in India using climate inputs. J Geophys Res Atmos 114(2):1–15.  https://doi.org/10.1029/2008JD010485CrossRefGoogle Scholar
  8. Dhanya CT, Kumar DN (2009b) Data mining for evolving fuzzy association rules for predicting monsoon rainfall of India. J Intell Syst 15(1):25–51.  https://doi.org/10.1515/JISYS.2009.18.3.193CrossRefGoogle Scholar
  9. Husna NEA, Bari SH, Hussain MM, Rahman MTU, Rahman M (2016) Ground water level prediction using artificial neural network. Int J Hydrol Sci Technol 6.  https://doi.org/10.1504/IJHST.2016.079356
  10. Jovanovic BB, Reljin IS, Reljin BD (2004) Modified ANFIS architecture—improving efficiency of ANFIS technique. In: 2004 7th seminar on neural network applications in electrical engineering, 2004. NEUREL 2004, pp 215–220.  https://doi.org/10.1109/NEUREL.2004.1416577
  11. Lohani AK, Krishan G (2015) Groundwater level simulation using artificial neural network in Southeast, Punjab, India. J Geol Geophys 4(3).  https://doi.org/10.4172/jgg.1000206
  12. Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol 227:56–65CrossRefGoogle Scholar
  13. Maiti S, Tiwari RK (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environ Earth Sci 71(7):3147–3160.  https://doi.org/10.1007/s12665-013-2702-7CrossRefGoogle Scholar
  14. Mohanty S, Jha MK, Kumar A, Panda DK (2013) Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. J Hydrol 495:38–51.  https://doi.org/10.1016/j.jhydrol.2013.04.041CrossRefGoogle Scholar
  15. Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manag 24(9):1845–1865.  https://doi.org/10.1007/s11269-009-9527-xCrossRefGoogle Scholar
  16. Mukherjee A (2018) Groundwater of South Asia. Springer Nature, Singapore. ISBN 978-981-10-3888-4Google Scholar
  17. Sujay Raghavendra N, Deka PC (2015) Selection of optimal parameters : GRID search forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid wavelet packet—support vector regression forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid. Cogent Eng 26(1).  https://doi.org/10.1080/23311916.2014.999414
  18. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66.  https://doi.org/10.1016/j.jhydrol.2003.12.010CrossRefGoogle Scholar
  19. Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-Artificial Intelligence models in hydrology: a review. J Hydrol 514:358–377.  https://doi.org/10.1016/j.jhydrol.2014.03.057CrossRefGoogle Scholar
  20. Purkait B, Kadam SS, Das SK (2008) Application of artificial neural network model to study arsenic contamination in groundwater of Malda district, Eastern India. J Environ Inform 12(2):140–149.  https://doi.org/10.3808/jei.200800132CrossRefGoogle Scholar
  21. Sharma N, Harinarayan Z, Dheeraj T (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model Earth Syst Environ 1(3):1–8.  https://doi.org/10.1007/s40808-015-0027-0CrossRefGoogle Scholar
  22. Singh KP, Gupta S (2012) Artificial intelligence based modeling for predicting the disinfection by-products in water. Chemometr Intell Lab Syst 114:122–131.  https://doi.org/10.1016/j.chemolab.2012.03.014CrossRefGoogle Scholar
  23. Singh R, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng 11(2):101–109. Retrieved from http://ascelibrary.org/doi/abs/10.1061/(ASCE)1084-0699(2006)11:2(101)CrossRefGoogle Scholar
  24. Sudheer C, Shrivastava NA, Panigrahi BK, Mathur S (2011) Groundwater level forecasting using SVM-QPSO. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 7076 LNCS(Part 1), pp 731–741.  https://doi.org/10.1007/978-3-642-27172-4_86CrossRefGoogle Scholar
  25. Suryanarayana C, Sudheer C, Mahammood V, Panigrahi BK (2014) An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145:324–335.  https://doi.org/10.1016/j.neucom.2014.05.026CrossRefGoogle Scholar
  26. Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3(1):26–32CrossRefGoogle Scholar
  27. Vapnik VN (1998) Statistical learning theory. Wiley, New York, pp 252–257Google Scholar
  28. Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306.  https://doi.org/10.1016/j.jhydrol.2009.06.019CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pragnaditya Malakar
    • 1
  • Abhijit Mukherjee
    • 1
    • 2
    • 3
    Email author
  • Sudeshna Sarkar
    • 4
  1. 1.Department of Geology and GeophysicsIndian Institute of Technology (IIT)—KharagpurKharagpurIndia
  2. 2.School of Environmental Science and EngineeringIndian Institute of Technology (IIT)—KharagpurKharagpurIndia
  3. 3.Applied Policy Advisory to Hydrogeosciences GroupIndian Institute of Technology (IIT)—KharagpurKharagpurIndia
  4. 4.Department of Computer Science and EngineeringIndian Institute of Technology (IIT)—KharagpurKharagpurIndia

Personalised recommendations