Skip to main content

Advertisement

SpringerLink
Log in

Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

The present study has applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India. In view of this, thirty-four (34) representative groundwater samples have been collected and analyzed for major cations and anions during pre- and post-monsoon seasons of 2015. The physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, Na, K, Cl, HCO3, SO4, NO3 and PO4 were considered for computing water quality index (WQI). Analytical results confirmed that all the parameters are within acceptable range; however, EC, TDS, TH, Ca and Mg are exceeding the desirable limit of the WHO drinking standards. The groundwater suitability for drinking was ascertained by WQI method. The WQI value ranges from 25.75 to 129.07 and from 37.54 to 91.38 in pre- and post-monsoon seasons, respectively. Only one sample (DW5) shows 129.07 WQI value indicating poor quality for drinking due to input of domestic and agricultural waste. In the view of generating consistent and precise model for prediction of WQI-based groundwater quality, a Levenberg–Marquardt three-layer back propagation algorithm was used in ANN architecture. Further, MLR model is used to check the efficiency of ANN prediction. The results corroborated that predictions of ANN model are satisfactory and confirms consistently acceptable performance for both the seasons. The proposed ANN model may be useful in similar studies of groundwater quality prediction for drinking purpose.

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

Similar content being viewed by others

References

  • Adimalla N (2018) Groundwater quality for drinking and irrigation purposes and potential health risks assessment: a case study from semi-arid region of South India. Exposure Health. https://doi.org/10.1007/s12403-018-0288-8

    Article  Google Scholar 

  • Al-Aboodi AH, Abbas SA, Ibrahim HT (2018) Effect of Hartha and Najibia power plants on water quality indices of Shatt Al-Arab River, south of Iraq. Appl Water Sci 8:64. https://doi.org/10.1007/s13201-018-0703-0

    Article  Google Scholar 

  • APHA, Federation and WE and American Public Health Association (2005) Standard methods for the examination of water and wastewater 2005. American Public Health Association (APHA), Washington

    Google Scholar 

  • Banerjee P, Ghose MK, Pradhan R (2018) AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya. Appl Water Sci 8:72. https://doi.org/10.1007/s13201-018-0699-5

    Article  Google Scholar 

  • Barzegar R, Asghari Moghaddam A (2016) Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Model Earth Syst Environ 2:26. https://doi.org/10.1007/s40808-015-0072-8

    Article  Google Scholar 

  • Beane JE, Turner CA, Hooper PR et al (1986) Stratigraphy, composition and form of the Deccan Basalts, Western Ghats, India. Bull Volcanol 48:61–83. https://doi.org/10.1007/BF01073513

    Article  Google Scholar 

  • Beaumont C, Makridakis S, Wheelwright SC, McGee VE (1984) Forecasting: methods and applications. J Oper Res Soc 35:79. https://doi.org/10.2307/2581936

    Article  Google Scholar 

  • Bhargava D (1983) Use of water quality index for river classification and zoning of Ganga river. Environ Pollut Ser B Chem Phys 6:51–67. https://doi.org/10.1016/0143-148X(83)90029-0

    Article  Google Scholar 

  • Brown A, Matlock MD (2011) A review of water scarcity indices and methodologies. University of Arkansas. The sustainability consortium, Fayetteville, p 21

    Google Scholar 

  • Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index-do we dare? water sewage. Works, October, pp 339–343

  • Darbandi S, Pourhosseini FA (2018) River flow simulation using a multilayer perceptron-firefly algorithm model. Appl Water Sci 8:85. https://doi.org/10.1007/s13201-018-0713-y

    Article  Google Scholar 

  • Dzwairo B, Hoko Z, Love D, Guzha E (2006) Assessment of the impacts of pit latrines on groundwater quality in rural areas: a case study from Marondera district. Zimbabwe Phys Chem Earth 31:779–788. https://doi.org/10.1016/j.pce.2006.08.031

    Article  Google Scholar 

  • Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11(3):645–656

    Article  Google Scholar 

  • Gaikwad S, Gaikwad S, Meshram D, Wagh V, Kandekar A, Kadam A (2019) Geochemical mobility of ions in groundwater from the tropical western coast of Maharashtra, India: implication to groundwater quality. Environ Dev Sustain 1–34

  • Gazzaz NM, Yusoff MK, Aris AZ et al (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64:2409–2420. https://doi.org/10.1016/j.marpolbul.2012.08.005

    Article  Google Scholar 

  • Hassan M, Zaffar H, Mehmood I, Khitab A (2018) Development of streamflow prediction models for a weir using ANN and step-wise regression. Model Earth Syst Environ 4(3):1021–1028

    Article  Google Scholar 

  • Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306

    Google Scholar 

  • Huang S-C, Huang Y-F (1990) Learning algorithms for perceptions using back-propagation with selective updates. IEEE Control Syst Mag. https://doi.org/10.1109/37.55125

    Article  Google Scholar 

  • Javdanian H (2017) Assessment of shear stiffness ratio of cohesionless soils using neural modeling. Model Earth Syst Environ 3(3):1045–1053

    Article  Google Scholar 

  • Jeong CH (2001) Effect of land use and urbanization on hydrochemistry and contamination of groundwater from Taejon area. Korea J Hydrol 253:194–210. https://doi.org/10.1016/S0022-1694(01)00481-4

    Article  Google Scholar 

  • Kadam AK, Jaweed TH, Umrikar BN et al (2017) Morphometric prioritization of semi-arid watershed for plant growth potential using GIS technique. Model Earth Syst Environ 3:1663–1673. https://doi.org/10.1007/s40808-017-0386-9

    Article  Google Scholar 

  • Kadam A, Karnewar AS, Umrikar B, Sankhua RN (2018) Hydrological response-based watershed prioritization in semiarid, basaltic region of western India using frequency ratio, fuzzy logic and AHP method. Environ Dev Sustain

  • Kaurish FW, Younos T (2007) Developing a standardized water quality index for evaluating surface water quality. J Am Water Resour Assoc 43:533–545. https://doi.org/10.1111/j.1752-1688.2007.00042.x

    Article  Google Scholar 

  • Khalil B, Ouarda TBMJ, St-Hilaire A (2011) Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydrol 405:277–287. https://doi.org/10.1016/j.jhydrol.2011.05.024

    Article  Google Scholar 

  • Kiraz A, Canpolat O, Erkan EF, Özer Ç (2018) Artificial neural networks modeling for the prediction of Pb (II) adsorption. Int J Environ Sci Technol 1–8

  • Logeshkumaran A, Magesh NS, Godson PS, Chandrasekar N (2014) Hydro-geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India. https://doi.org/10.1007/s13201-014-0196-4

  • Malinova T, Guo ZX (2004) Artificial neural network modelling of hydrogen storage properties of Mg-based alloys. Mater Sci Eng A 365:219–227. https://doi.org/10.1016/j.msea.2003.09.031

    Article  Google Scholar 

  • Moon SK, Woo NC, Lee KS (2004) Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge. J Hydrol 292(1–4):198–209

    Article  Google Scholar 

  • Mladenović-Ranisavljević II, Žerajić SA (2017) Comparison of different models of water quality index in the assessment of surface water quality. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-017-1426-8

    Article  Google Scholar 

  • Mukate S, Panaskar D, Wagh V et al (2017) Impact of anthropogenic inputs on water quality in Chincholi industrial area of Solapur, Maharashtra, India. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2017.11.001

    Article  Google Scholar 

  • Panaskar DB, Wagh VM, Muley AA et al (2016) Evaluating groundwater suitability for the domestic, irrigation, and industrial purposes in Nanded Tehsil, Maharashtra, India, using GIS and statistics. Arab J Geosci. https://doi.org/10.1007/s12517-016-2641-1

    Article  Google Scholar 

  • Pawar NJ, Pawar JB, Kumar S, Supekar A (2008) Geochemical eccentricity of ground water allied to weathering of basalts from the Deccan Volcanic Province, India: insinuation on CO2 consumption. Aquat Geochem 14(1):41–71

    Article  Google Scholar 

  • Pesce SF, Wunderlin DA (2000) Use of Water Quality Indices To Verify the  Rdoba City (Argentina) on Suquõâ a Impact of Co. Science 80: 34

    Google Scholar 

  • Sahu P, Sikdar PK (2008) Hydrochemical framework of the aquifer in and around East Kolkata Wetlands, West Bengal, India. Environ Geol 55(4):823–835

    Article  Google Scholar 

  • Sahoo S, Jha MK (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeol J 21(8):1865–1887

    Article  Google Scholar 

  • Sakizadeh M (2016) Artificial intelligence for the prediction of water quality index in groundwater systems. Model Earth Syst Environ 2(1):8

    Article  Google Scholar 

  • Salari M, Salami Shahid E, Afzali SH, Ehteshami M, Conti GO, Derakhshan Z, Sheibani SN (2018) Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water. Food Chem Toxicol 118:212–219. https://doi.org/10.1016/j.fct.2018.04.036

    Article  Google Scholar 

  • Şener Ş, Şener E, Davraz A (2017) Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci Total Environ 584–585:131–144. https://doi.org/10.1016/j.scitotenv.2017.01.102

    Article  Google Scholar 

  • Sharma N, Zakaullah M, Tiwari H, Kumar D (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model Earth Syst Environ 1(3):23

    Article  Google Scholar 

  • Shooshtarian MR, Dehghani M, Margherita F, Gea OC, Mortezazadeh S (2018) Land use change and conversion effects on ground water quality trends: an integration of land change modeler in GIS and a new ground water quality index developed by fuzzy multi-criteria group decision-making models. Food Chem Toxicol 114:204–214

    Article  Google Scholar 

  • Sihag P (2018) Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Model Earth Syst Environ 4(1):189–198

    Article  Google Scholar 

  • Varol S, Davraz A (2014) Evaluation of the groundwater quality with WQI (water quality index) and multivariate analysis: a case study of the Tefenni plain (Burdur/Turkey). Environ Earth Sci 73:1725–1744. https://doi.org/10.1007/s12665-014-3531-z

    Article  Google Scholar 

  • Vasant W, Dipak P, Aniket M, Ranjitsinh P, Shrikant M, Nitin D, Manesh A, Abhay V (2016) GIS and statistical approach to assess the groundwater quality of Nanded Tehsil, (MS) India. In: Proceedings of first international conference on information and communication technology for intelligent systems, vol 1, pp 409–417. Springer, Cham

  • Wagh VM, Panaskar DB, Varade AM et al (2016a) Major ion chemistry and quality assessment of the groundwater resources of Nanded tehsil, a part of southeast Deccan Volcanic Province, Maharashtra, India. Environ Earth Sci. https://doi.org/10.1007/s12665-016-6212-2

    Article  Google Scholar 

  • Wagh VM, Panaskar DB, Muley AA et al (2016b) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India. Model Earth Syst Environ 2:196. https://doi.org/10.1007/s40808-016-0250-3

    Article  Google Scholar 

  • Wagh VM, Panaskar DB, Muley AA, Mukate SV (2017a) Groundwater suitability evaluation by CCME WQI model for Kadava River Basin, Nashik, Maharashtra, India. Model Earth Syst Environ 3:557–565. https://doi.org/10.1007/s40808-017-0316-x

    Article  Google Scholar 

  • Wagh VM, Panaskar DB, Muley AA (2017b) Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Model Earth Syst Environ 3(1):36

    Article  Google Scholar 

  • Wagh V, Panaskar D, Mukate S, Lolage Y, Muley A (2017c) Groundwater quality evaluation by physicochemical characterization and water quality index for Nanded Tehsil, Maharashtra, India. In: Proceedings of the 7th international conference on biology, environment and chemistry, IPCBEE, vol 98

  • Wagh VM, Panaskar DB, Mukate SV, Gaikwad SK, Muley AA, Varade AM (2018a) Health risk assessment of heavy metal contamination in groundwater of Kadava River Basin, Nashik, India. Model Earth Syst Environ 4(3):969–980

    Article  Google Scholar 

  • Wagh V, Panaskar D, Aamalawar M, Lolage Y, Mukate S, Adimalla N (2018b) Hydrochemical characterisation and groundwater suitability for drinking and irrigation uses in semiarid region of Nashik, Maharashtra, India. Hydrospat Anal 2 (1): 43–60, https://doi.org/10.21523/gcj3.18020104

    Article  Google Scholar 

  • Wagh V, Panaskar D, Muley A et al (2018c) Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India. Groundw Sustain Dev. https://doi.org/10.1016/j.gsd.2017.12.012

    Article  Google Scholar 

  • Wagh VM, Panaskar DB, Mukate SV, Aamalawar ML, Sahu L, U (2019) Nitrate associated health risks from groundwater of Kadava River Basin Nashik, Maharashtra, India. Hum Ecol Risk Assess 1–19

  • Wan Abdul Ghani WMH, Abas Kutty A, Mahazar MA et al (2018) Performance of biotic indices in comparison to chemical-based water quality index (WQI) in evaluating the water quality of urban river. Environ Monit Assess. https://doi.org/10.1007/s10661-018-6675-6

    Article  Google Scholar 

  • WHO (2011) Guidelines for drinking-water quality, 4th edn. http://www.whqlibdoc.who.int/publications/2011/9789241548151_eng.pdf

  • Yaseen ZM, Fu M, Wang C et al (2018) Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons. Water Resour Manag 32:1883–1899. https://doi.org/10.1007/s11269-018-1909-5

    Article  Google Scholar 

  • Yidana SM, Banoeng-Yakubo B, Akabzaa TM (2010) Analysis of groundwater quality using multivariate and spatial analyses in the Keta basin, Ghana. J Afr Earth Sci 58:220–234. https://doi.org/10.1016/j.jafrearsci.2010.03.003

    Article  Google Scholar 

  • Yilma M, Kiflie Z, Windsperger A, Gessese N (2018) Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia. Model Earth Syst Environ 4:175–187. https://doi.org/10.1007/s40808-018-0437-x

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their sincere thanks to Head, Departments of Geology and Environmental Sciences Savitribai Phule, Pune University, Pune for providing necessary facilities for analysis. In addition, thanks to anonym reviewers for meaningful suggestions to enhance the manuscript quality.

Author information

Authors and Affiliations

  1. Department of Environmental Sciences, Savitribai Phule Pune University, Pune, 411007, India

    A. K. Kadam

  2. School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

    V. M. Wagh

  3. School of Mathematical Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

    A. A. Muley

  4. Department of Geology, Savitribai Phule Pune University, Pune, 411007, India

    B. N. Umrikar

  5. Basin Planning, Central Water Commission, New Delhi, 110066, India

    R. N. Sankhua

Authors
  1. A. K. Kadam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. M. Wagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. A. Muley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. N. Umrikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. N. Sankhua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. M. Wagh.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kadam, A.K., Wagh, V.M., Muley, A.A. et al. Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Model. Earth Syst. Environ. 5, 951–962 (2019). https://doi.org/10.1007/s40808-019-00581-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-019-00581-3

Keywords

Search

Navigation