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An integration of geospatial and machine learning techniques for mapping groundwater potential: a case study of the Shipra river basin, India

Abstract

Groundwater is an important component of the hydrologic cycle and its significance is quite high due to the lack of surface water and is an important source of fresh water. The amount of surface water alone is not enough to meet the demands of increasing population and increased needs for different purposes due to technological advances. Hence, the need of the hour is to increase groundwater sources and manage them effectively for their sustainable growth. Therefore, the main objective of this study is to map groundwater potential (GWP) zones of the Shipra river basin in India using advanced machine learning and geospatial techniques. Nine factors were used as effective factors such as slope degree, altitude, plan curvature, topographic wetness index (TWI), profile curvature, topographic factor, drainage density, slope aspect, and land use/land cover. The models adopted in this study were classification and regression tree (CART), boosted regression tree (BRT), and random forest (RF). The integrated results of GIS and machine learning techniques were proved to be effective and successful in predicting GWP zones. The area under the curve (AUC) of three models namely BRT, CART, and RF came out to be 0.841, 0.880, and 0.899, respectively. This indicates that all the models are giving a good performance for the GWP zone mapping (>0.80). This study also found that the best technique for prediction is random forest followed by CART and BRT in the case of Shipra river basin. Therefore, this study outcome can prove to be beneficial for effective management, protection, and exploration of groundwater prospects for the different stakeholders.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

References

  • Adeyeye OA, Ikpokonte EA, Arabi SA (2019) GIS-based groundwater potential mapping within Dengi area, North Central Nigeria. Egypt J Remote Sens Sp Sci 22:175–181

    Google Scholar 

  • Arabameri A, Lee S, Tiefenbacher JP, Ngo PTT (2020) Novel ensemble of MCDM-Artificial Intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran). Remote Sens 12:490

    Article  Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci J 24:43–69

    Article  Google Scholar 

  • Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM (2016) mlr: machine learning in R. J Mach Learn Res 17:5938–5942

    Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Breiman L, Friedman J, Stone CJ,Olshen RA (1984) Classification and regression trees. (1st ed.). Chapman andHall/CRC press. 368 Pages.https://doi.org/10.1201/9781315139470

  • Briem GJ, Benediktsson JA, Sveinsson JR (2002) Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sens 40:2291–2299

    Article  Google Scholar 

  • Chou JS, Ho CC, Hoang HS (2018) Determining quality of water in reservoir using machine learning. Ecol Inform 44:57–75

    Article  Google Scholar 

  • Clapcott J, Goodwin E, Snelder T(2013) Predictive models of benthic macroinvertebrate metrics. Prepared forMinistry for the Environment. Cawthron Report No. 2301. p. 35

  • Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139–157

    Article  Google Scholar 

  • Edet AE, Okereke CS (1997) Assessment of hydrogeological conditions in basement aquifers of the Precambrian Oban massif, southeastern Nigeria. J Appl Geophys 36:195–204

    Article  Google Scholar 

  • Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: a survey. Comput Electron Agric 155:257–282

    Article  Google Scholar 

  • Fashae OA, Tijani MN, Talabi AO, Adedeji OI (2014) Delineation of groundwater potential zones in the crystalline basement terrain of SW-Nigeria: an integrated GIS and remote sensing approach. Appl Water Sci 4:19–38

    Article  Google Scholar 

  • Ganapuram S, Kumar GV, Krishna IM, Kahya E, Demirel MC (2009) Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS. Adv Eng Softw 40:506–518

    Article  Google Scholar 

  • Guru B, Seshan K, Bera S (2017) Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. J King Saud Univ 29:333–347

    Article  Google Scholar 

  • Jaiswal S, Galkate R, Chandola VK, Singh P, Thakur A (2019) Assessment of drought in Shipra River Basin, Madhya Pradesh. J Soil Water Conserv 18:188–195

    Article  Google Scholar 

  • Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science (80- ) 349:255–260

    Article  Google Scholar 

  • Kaushik P, Ghosh P (2018) Morphometric analysis of Shipra River sub-basin, India, remote sensing and GIS approach. Int J Creat Res Thoughts 6:1536–1546

    Google Scholar 

  • Khadka G, Pathak D (2021) Groundwater potential as an indicator of water poverty index in drought-prone mid-hill region of Nepal Himalaya. Groundw Sustain Dev 12:100502

    Article  Google Scholar 

  • Kumar P, Herath S, Avtar R, Takeuchi K (2016) Mapping of groundwater potential zones in Killinochi area, Sri Lanka, using GIS and remote sensing techniques. Sustain Water Resour Manag 2:419–430

    Article  Google Scholar 

  • Kumar YY, Moorthy DVS, Srinivas GS (2017) Identification of groundwater potential zones using remote sensing and geographical information system. Int J Civ Eng Technol (IJCIET) 8:1–10

    Google Scholar 

  • Lee S, Song KY, Kim Y, Park I (2012) Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J 20:1511–1527

    Article  Google Scholar 

  • Lee S, Hong SM, Jung HS (2018) GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto Int 33:847–861

    Article  Google Scholar 

  • Lee S, Hyun Y, Lee MJ (2019) Groundwater potential mapping using data mining models of big data analysis in Goyang-si, South Korea. Sustainability 11:1678

    Article  Google Scholar 

  • Lee S, Hyun Y, Lee S, Lee MJ (2020a) Groundwater potential mapping using remote sensing and GIS-based machine learning techniques. Remote Sens 12:1200

    Article  Google Scholar 

  • Lee S, Jang J, Kim Y, Cho N, Lee MJ (2020b) Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method. Remote Sens 12:2663

    Article  Google Scholar 

  • Lillesand T, Kiefer RW, Chipman J (2015) Remote sensing and image interpretation. John Wiley & Sons

  • Mahalingam B, Vinay M (2015) Identification of ground water potential zones using GIS and remote sensing techniques: a case study of Mysore taluk-Karnataka. Int J Geomatics Geosci 5:393–403

    Google Scholar 

  • Martínez-Santos P, Renard P (2020) Mapping groundwater potential through an ensemble of big data methods. Groundwater 58:583–597

    Article  Google Scholar 

  • Masroor M, Rehman S, Sajjad H, Rahaman MH, Sahana M, Ahmed R, Singh R (2021) Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm. Groundw Sustain Dev 13:100554

    Article  Google Scholar 

  • McKay G, Harris JR (2016) Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: a case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Nat Resour Res 25:125–143

    Article  Google Scholar 

  • Miao X, Heaton JS, Zheng S, Charlet DA, Liu H (2012) Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data. Int J Remote Sens 33:1823–1849

    Article  Google Scholar 

  • Mitchell TM (1997) Artificial neural networks. Mach learn 45:81–127

    Google Scholar 

  • Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arab J Geosci 8:913–929

    Article  Google Scholar 

  • Moghaddam DD, Rahmati O, Panahi M, Tiefenbacher J, Darabi H, Haghizadeh A, Haghighi AT, Nalivan OA, Bui DT (2020) The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena 187:104421

    Article  Google Scholar 

  • Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour Res 22:1350–1360

    Article  Google Scholar 

  • Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29:5217–5236

    Article  Google Scholar 

  • Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O (2017) A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J Hydrol 548:471–483

    Article  Google Scholar 

  • Nampak H, Pradhan B, Abd Manap M (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Jaafari A, Nguyen HD, Van Phong T, Al-Ansari N, Prakash I, Le HV, Pham BT (2020) Groundwater potential mapping combining artificial neural network and real AdaBoost ensemble technique: the DakNong Province case-study, Vietnam. Int J Environ Res Public Health 17:2473

    Article  Google Scholar 

  • Nsiah E, Appiah-Adjei EK, Adjei KA (2018) Hydrogeological delineation of groundwater potential zones in the Nabogo basin, Ghana. J African Earth Sci 143:1–9

    Article  Google Scholar 

  • NWM (2011) TA 7417- IND: support for the NationalAction Plan on Climate Change Support to the National Water Mission. Governmentof India Governments of Punjab. Madhya Pradesh, Tamil Nadu and AsianDevelopment Bank. https://www.adb.org/sites/default/files/project-document/60533/43169-012-ind-dpta-05.pdf. Accessed 19 Jul 2021

  • Obeidavi S, Gandomkar M, Akbarizadeh G, Delfan H (2021) Evaluation of groundwater potential using Dempster-Shafer model and sensitivity analysis of effective factors: a case study of north Khuzestan province. Remote Sens Appl Soc Environ 22:100475

    Google Scholar 

  • Oh HJ, Kim YS, Choi JK, Park E, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172

    Article  Google Scholar 

  • Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. Q Rev Biol 83:171–193

    Article  Google Scholar 

  • Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222

    Article  Google Scholar 

  • Paryani S, Neshat A, Javadi S, Pradhan B (2020a) GIS-based comparison of the GA-LR ensemble method and statistical models at Sefiedrood Basin, Iran. Arab J Geosci 13:1–17

    Article  Google Scholar 

  • Paryani S, Neshat A, Javadi S, Pradhan B (2020b) Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping. Nat Hazards 103:1961–1988

    Article  Google Scholar 

  • Prasad P, Loveson VJ, Kotha M, Yadav R (2020) Application of machine learning techniques in groundwater potential mapping along the west coast of India. GIScience Remote Sens 57:735–752

    Article  Google Scholar 

  • Sachdeva S, Kumar B (2021) Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India. Stoch Environ Res Risk Assess 35:287–306

    Article  Google Scholar 

  • Sander P, Chesley MM, Minor TB (1996) Groundwater assessment using remote sensing and GIS in a rural groundwater project in Ghana: lessons learned. Hydrogeol J 4:40–49

    Article  Google Scholar 

  • Schapire RE (2003) The boostingapproach to machine learning: An overview. In: Denison DD, Hansen MH, HolmesCC, Mallick B, Yu B (eds) Nonlinear Estimation and Classification. LectureNotes in Statistics, vol 171. Springer, New York, NY.https://doi.org/10.1007/978-0-387-21579-2_9

  • Senthil Kumar GR, Shankar K (2014) Assessment of groundwater potential zones using GIS. Front Geosci 2:1–10

    Google Scholar 

  • Singh LK, Jha MK, Chowdary VM (2018) Assessing the accuracy of GIS-based multi-criteria decision analysis approaches for mapping groundwater potential. Ecol Indic 91:24–37

    Article  Google Scholar 

  • Singha S, Das P, Singha SS (2021) A fuzzy geospatial approach for delineation of groundwater potential zones in Raipur district, India. Groundw Sustain Dev 12:100529

    Article  Google Scholar 

  • Srivastava PK, Bhattacharya AK (2006) Groundwater assessment through an integrated approach using remote sensing, GIS and resistivity techniques: a case study from a hard rock terrain. Int J Remote Sens 27:4599–4620

    Article  Google Scholar 

  • Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14:323–348

    Article  Google Scholar 

  • Suganthi S, Elango L, Subramanian SK (2013) Groundwater potential zonation by remote sensing and GIS techniques and its relation to the groundwater level in the coastal part of the Arani and Koratalai River Basin, Southern India. Earth Sci Res J 17:87–95

    Google Scholar 

  • Taylor R, Howard K (2000) A tectono-geomorphic model of the hydrogeology of deeply weathered crystalline rock: evidence from Uganda. Hydrogeol J 8:279–294

    Article  Google Scholar 

  • Teeuw RM (1995) Groundwater exploration using remote sensing and a low-cost geographical information system. Hydrogeol J 3:21–30

    Article  Google Scholar 

  • Tolche AD (2021) Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia. Geol Ecol Landscapes 5:65–80

    Article  Google Scholar 

  • Waikar ML, Nilawar AP (2014) Identification of groundwater potential zone using remote sensing and GIS technique. Int J Innov Res Sci Eng Technol 3:12163–12174

    Google Scholar 

  • Wang Z, Lai C, Chen X, Yang B, Zhao S, Bai X (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141

    Article  Google Scholar 

  • Zhu Q, Abdelkareem M (2021) Mapping groundwater potential zones using a knowledge-driven approach and GIS analysis. Water 13:579

    Article  Google Scholar 

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Acknowledgements

The authors thankfully acknowledge the Central Groundwater Board (CGWB) for providing the required data. We also acknowledge the USGS Earth Resources Observation portal for providing necessary data. We also acknowledge Indian Institute of Technology Roorkee for providing financial support. We would also like to thank the anonymous reviewers and editors for their critical comments which significantly improved the manuscript.

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Correspondence to Santosh Murlidhar Pingale.

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Patidar, R., Pingale, S.M. & Khare, D. An integration of geospatial and machine learning techniques for mapping groundwater potential: a case study of the Shipra river basin, India. Arab J Geosci 14, 1645 (2021). https://doi.org/10.1007/s12517-021-07871-0

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  • DOI: https://doi.org/10.1007/s12517-021-07871-0

Keywords

  • Groundwater potential zones
  • Classification and regression tree
  • Machine learning techniques
  • Boosted regression tree
  • Random forest
  • GIS