10.1007/s40095-022-00521-5

Artificial neural network-based assessment of water quality index (WQI) of surface water in Gwalior-Chambal region

  • Shyamveer Singh Chauhan*1,
  • Manoj Kumar Trivedi1,
  1. Civil Engineering Department, MITS Gwalior, Gwalior, IN

Published in Issue 2022-08-24

How to Cite

Chauhan, S. S., & Trivedi, M. K. (2022). Artificial neural network-based assessment of water quality index (WQI) of surface water in Gwalior-Chambal region. International Journal of Energy and Environmental Engineering, 14(1 (March 2023). https://doi.org/10.1007/s40095-022-00521-5
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Abstract In the complex environment of physical, chemical, and biological processes in water bodies, it is always challenging to model the water quality while dealing with nonlinear behaviour of water quality variables. To assess the surface water quality of Gwalior-Chambal region, India, this paper applies the artificial neural network (ANN) process as it has been proven an efficient process to handle the nonlinear data. During the pre- and post-monsoon season of 2021, a total 49 surface water samples in each season were collected and examined for various water quality parameters such as Turbidity, pH, Total Hardness, Total Alkalinity, Total Dissolved Solids, Chloride, SO 4 , NO 3 , PO 4 , DO, BOD, COD, and MPN. Noteworthy, all surface water sources were found unfit for drinking as the MPN value of all water samples was estimated more  than zero with mean value of 481.88 and 239.20 for pre- and post-monsoon season, respectively. The value of ANN based water quality index (WQI) ranges from 27.79 to 103.42 and 33.78 to 96.70 for pre- and post-monsoon seasons, respectively. In order to develop a consistent and accurate ANN based WQI model, the paper applies Levenberg–Marquardt's three-layer backpropagation technique in ANN structures that contains 13 contributing neurons, nine hidden neurons, and one output variable. Computed values of performance statistics justify the accuracy of developed model over multiple linear regression model in predicting the WQI in both seasons. The developed model is also expected to be useful for other than Gwalior-Chambal region for surface water quality assessment.

Keywords

  • Water quality index,
  • Artificial neural network,
  • Surface water,
  • Gwalior-Chambal region

References

  1. Sharma et al. (2017) Elsevier Inc. https://doi.org/10.1016/B978-0-12-809330-6.00005-2
  2. Aldhyani et al. (2020) Water quality prediction using artificial intelligence algorithms https://doi.org/10.1155/2020/6659314
  3. Mukate et al. (2018) Impact of anthropogenic inputs on water quality in Chincholi industrial area of Solapur, Maharashtra, India (pp. 359-371) https://doi.org/10.1016/J.GSD.2017.11.001
  4. Adimalla (2018) Groundwater quality for drinking and irrigation purposes and potential health risks assessment : a case study from semi - arid region of South India https://doi.org/10.1007/s12403-018-0288-8
  5. Gaikwad et al. (2019) . Springer
  6. Moon et al. (2004) Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge (pp. 198-209) https://doi.org/10.1016/j.jhydrol.2003.12.030
  7. Krishna kumar et al. (2015) Hydro-geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India 5(4) (pp. 335-343) https://doi.org/10.1007/s13201-014-0196-4
  8. Sharma et al. (2014) Elsevier Inc. https://doi.org/10.1016/B978-0-12-411645-0.00003-1
  9. Horton (1965) An index number system for rating water quality 37(3) (pp. 300-306)
  10. Tyagi, S., Sharma, B., Singh, P., Dobhal, R.: Water quality assessment in terms of water quality index. 1(3), pp. 34–38 (2013).
  11. https://doi.org/10.12691/ajwr-1-3-3
  12. .
  13. Brown and Matlock (2011) A review of water scarcity indices and methodologies 106(1)
  14. Bhargava (1983) Use of a water quality index for river classification and zoning of Ganga River (pp. 51-67) https://doi.org/10.1016/0143-148X(83)90029-0
  15. Kaurish and Younos (2007) Developing a standardized water quality index for evaluating surface water quality 43(2) (pp. 533-545) https://doi.org/10.1111/j.1752-1688.2007.00042.x
  16. Banerjee et al. (2018) AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya https://doi.org/10.1007/s13201-018-0699-5
  17. Al-Aboodi et al. (2018) Effect of Hartha and Najibia power plants on water quality indices of Shatt Al-Arab River, south of Iraq https://doi.org/10.1007/s13201-018-0703-0
  18. Mladenović-Ranisavljević and Žerajić (2018) Comparison of different models of water quality index in the assessment of surface water quality 15(3) (pp. 665-674) https://doi.org/10.1007/s13762-017-1426-8
  19. Shooshtarian et al. (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 (pp. 204-214) https://doi.org/10.1016/j.fct.2018.02.025
  20. Swamee and Tyagi (2007) Improved method for aggregation of water quality subindices 133(2) (pp. 220-225) https://doi.org/10.1061/(asce)0733-9372(2007)133:2(220)
  21. Nayak et al. (2020) Development of water quality index for Godavari River (India) based on fuzzy inference system 10(December) https://doi.org/10.1016/j.gsd.2020.100350
  22. Sibanda and Pretorius (2012) Artificial neural networks-a review of applications of neural networks in the modeling of HIV epidemic 44(April) (pp. 975-8887)
  23. Sarkar and Pandey (2015) River water quality modelling using artificial neural network technique 4(Icwrcoe) (pp. 1070-1077) https://doi.org/10.1016/j.aqpro.2015.02.135
  24. Chen and Chau (2016) A hybrid double feedforward neural network for suspended sediment load estimation 30(7) (pp. 2179-2194) https://doi.org/10.1007/s11269-016-1281-2
  25. Anmala et al. (2015) GIS and artificial neural network-based water quality model for a stream network in the upper Green River Basin, Kentucky, USA 141(5) https://doi.org/10.1061/(asce)ee.1943-7870.0000801
  26. Wu et al. (2014) Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (pp. 108-127) https://doi.org/10.1016/j.envsoft.2013.12.016
  27. Haykin (1999) Prantice Hall
  28. Gazzaz et al. (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors 64(11) (pp. 2409-2420) https://doi.org/10.1016/j.marpolbul.2012.08.005
  29. Barzegar and AsghariMoghaddam (2016) Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction https://doi.org/10.1007/s40808-015-0072-8
  30. Darbandi and Pourhosseini (2018) River flow simulation using a multilayer perceptron-firefly algorithm model https://doi.org/10.1007/s13201-018-0713-y
  31. Kiraz et al. (2019) Artificial neural networks modeling for the prediction of Pb(II) adsorption 16(9) (pp. 5079-5086) https://doi.org/10.1007/s13762-018-1798-4
  32. Emamgholizadeh et al. (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models 11(3) (pp. 645-656) https://doi.org/10.1007/s13762-013-0378-x
  33. Antanasijević et al. (2014) Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis (pp. 1895-1907) https://doi.org/10.1016/J.JHYDROL.2014.10.009
  34. Salami and Ehteshami (2015) Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers) 12(10) (pp. 3235-3242) https://doi.org/10.1007/s13762-015-0800-7
  35. Wagh et al. (2018) Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India (pp. 436-445) https://doi.org/10.1016/j.gsd.2017.12.012
  36. Gupta et al. (2019) Application of ANN for water quality index 9(5) (pp. 688-693) https://doi.org/10.18178/ijmlc.2019.9.5.859
  37. Keskin et al. (2015) Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey) 73(9) (pp. 5333-5347) https://doi.org/10.1007/s12665-014-3784-6
  38. Yilma et al. (2018) Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia 4(1) (pp. 175-187) https://doi.org/10.1007/s40808-018-0437-x
  39. Salari (2018) Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water (pp. 212-219) https://doi.org/10.1016/j.fct.2018.04.036
  40. Wagh et al. (2016) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India 2(4) (pp. 1-10) https://doi.org/10.1007/s40808-016-0250-3
  41. Wagh et al. (2017) Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model https://doi.org/10.1007/s40808-017-0290-3
  42. Sinshaw et al. (2019) Artificial Neural Network for Prediction of Total Nitrogen and Phosphorus in US Lakes 145(6) https://doi.org/10.1061/(asce)ee.1943-7870.0001528
  43. Kadam et al. (2019) Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India 5(3) (pp. 951-962) https://doi.org/10.1007/s40808-019-00581-3
  44. Nayak et al. (2021) Artificial neural network based water quality index (WQI) for river Godavari (India) https://doi.org/10.1016/j.matpr.2021.03.100
  45. Othman (2020) “Mechanics Efficient river water quality index prediction considering minimal number of inputs variables https://doi.org/10.1080/19942060.2020.1760942
  46. Sharma et al. (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed 1(3) (pp. 1-8) https://doi.org/10.1007/s40808-015-0027-0
  47. Javdanian (2017) Assessment of shear stiffness ratio of cohesionless soils using neural modeling 3(3) (pp. 1045-1053) https://doi.org/10.1007/s40808-017-0351-7
  48. Sihag (2018) Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network 4(1) (pp. 189-198) https://doi.org/10.1007/s40808-018-0434-0
  49. 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 (2005)
  50. BIS, Bureau of Indian Standards Specification for drinking water. IS 10500:2012, New Delhi, India (2012)
  51. WHO Guidelines for drinking-water quality, 4th edn.
  52. http://www.whqlibdoc.who.int/publications/2011/9789241548151_eng.pdf
  53. (2011)
  54. Şener et al. (2017) Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey) (pp. 131-144) https://doi.org/10.1016/J.SCITOTENV.2017.01.102
  55. Yidana et al. (2010) Analysis of groundwater quality using multivariate and spatial analyses in the Keta basin, Ghana 58(2) (pp. 220-234) https://doi.org/10.1016/J.JAFREARSCI.2010.03.003
  56. Sahu and Sikdar (2008) Hydrochemical framework of the aquifer in and around East Kolkata Wetlands, West Bengal, India 55(4) (pp. 823-835) https://doi.org/10.1007/s00254-007-1034-x
  57. Najjar et al. (1996) On the identification of compaction characteristics by neuronets 18(3) (pp. 167-187) https://doi.org/10.1016/0266-352X(95)00030-E
  58. Dandy (2010) “Methods used for the development of neural networks for the prediction of water resource variables in river systems : current status and future directions https://doi.org/10.1016/j.envsoft.2010.02.003
  59. Sahoo and Jha (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment 21(8) (pp. 1865-1887) https://doi.org/10.1007/s10040-013-1029-5