Skip to main content
SpringerLink
Log in

Predictive Analysis of Lake Water Quality Using an Evolutionary Algorithm

  • Chapter
  • First Online:
Optimization in Machine Learning and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

  • 2050 Accesses

Abstract

Lakes are water bodies having considerable availability of water. Hence, it could be convincing and promising water resource in the area of acute shortage of water. Throughout the world, the water quality of lakes, natural or human-made, has been deteriorating because of urban, agricultural, industrial and other impacts. Widespread eutrophication of lakes leads to the overgrowth of plants and algae; the bacterial degradation of their biomass consumes more oxygen from water resulting in the state of hypoxia [Azhagesan in Water quality parameters and water quality standards for different uses. National Water Academy Report, 1]. Water quality monitoring of reservoirs is essential in the exploitation of aquatic resources and its conservation. Continuous monitoring of water quality of lakes and prediction of water quality will help to conserve the lakes. The authors offer an application of an evolutionary algorithm for predictive analysis of water quality of reservoirs/lakes and to discover a functional relationship between features in data (symbolic regression). We have used a nature-inspired technique of genetic programming (GP) as it can evolve the best individual (program). GP has also been used to discover a functional relationship between features in data (symbolic regression) and to group data into categories (classification). We have used monthly water quality data observed by Maharashtra Water Resources Department, Hydrological Data Users Group (HDUG) for the present study. Case study of Gangapur reservoir (an artificial lake) located in Nashik district in the state of Maharashtra, India, is used for demonstration of strengths of the evolutionary algorithm. We have developed cause-effect models for biochemical oxygen demand (BOD), chemical oxygen demand (COD) and faecal coliform bacteria (F-col). In situations of a scarcity of observed data, hybrid cause-effect models will be useful. We have used spectral analysis to eliminate the problem of time lag in case of hybrid models. Cross-validation is executed to ensure the overfitting of the models. We have performed performance evaluation by classification accuracy, mean absolute error and mean squared error. The evolved hybrid cause-effect model with spectral analysis is of immense use for small data sets.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Azhagesan R (1999) Water quality parameters and water quality standards for different uses. National Water Academy Report

    Google Scholar 

  2. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic Programming: an introduction, an automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers, Inc., San Francisco, California

    Book  Google Scholar 

  3. Bartram J, Ballance R, World Health Organization & United Nations Environment Programme (1996). Water quality monitoring: a practical guide to the design and implementation of freshwater quality studies and monitoring programs. In: Bartram J, Ballance R (eds). E & FN Spon, London. http://www.who.int/iris/handle/10665/41851

  4. Brameier M (2004) On linear genetic programming; Ph.D. thesis, University of Dortmund https://pdfs.semanticscholar.org/31c8/a5e106b80c07c1c0f74bcf42de6d24de2bf1.pdf

  5. Chavan A, Sharma MP, Bhargava R (2009) Water quality assessment of the Godavari river National conference on hydraulics. HydroNepal J Water Energy Environ 1:31–34. https://doi.org/10.3126/hn.v5i0.2483

    Article  Google Scholar 

  6. Coppola E, Rana A, Poultonx M, Szidarovszky F, Uhl V (2005) A neural network model for predicting aquifer water level elevations. Ground Water 43(2):231–243

    Article  Google Scholar 

  7. Dawson CW, Wilby RL (1999) Hydrological modelling using artificial neural networks. Prog Phys Geogr Earth Environ 25(01):80–108. https://doi.org/10.1177/030913330102500104

  8. Dogan E, Koklu R, Sengorur B (2009) Modelling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J Environ Manage 90(2):1229–1235

    Article  Google Scholar 

  9. Francone FD, Markus C, Banzhaf W, Nordin P (1999) Homologous crossover in genetic programming. Proc Genet Evol Comput Conf 2:1021–1026

    Google Scholar 

  10. Guven A (2009) Linear genetic programming for time-series modelling of daily flow rate. J Earth Syst Sci 118(02):137–146

    Article  Google Scholar 

  11. Hitoshi I, Yoshihiko H, Topon KP (2009) Applied genetic programming and machine learning. CRC Press International Series on Computational Intelligence, Boca Raton

    MATH  Google Scholar 

  12. Jadhav MS, Khare KC, Warke AS (2015) Water quality prediction of Gangapur reservoir (India) using LS-SVM and genetic programming. Lakes Reservoirs Res Manag 20(04):275–284. https://doi.org/10.1111/lre.12113

    Article  Google Scholar 

  13. Jadhav MS, Khare KC, Warke AS (2014) Selection of significant input parameters for water quality prediction-a comparative approach. Int J Res Advent Technol 2(03):81–90

    Google Scholar 

  14. Khovanova NA, Shaikhina T, Mallick KK (2015) Neural networks for analysis of trabecular bone in osteoarthritis. Bioinspired, Biomimetic Nanobiomaterials 4(1):90–100

    Article  Google Scholar 

  15. Koza JR (1992) Genetic programming: on the programming of computers using natural selection. A Bradford book. MIT Press, Cambridge, Massachusetts, London, England

    MATH  Google Scholar 

  16. Lebaron B, Weigend AS (1998) A bootstrap evaluation of the effect of data splitting on financial time series, IEEE Trans Neural Networks 213–220

    Google Scholar 

  17. Lermontov A, Yokoyama L, Lermontov M, Machado MAS (2009) River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecol Ind 9(6):1188–1197

    Google Scholar 

  18. Londhe SN, Dixit PR (2012) Genetic programming—new approaches and successful applications. In: Soto SV (ed) 8/12. In Tech Publications

    Google Scholar 

  19. Londhe S, Charhate S (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol Sci J 55(7):1163–1174

    Google Scholar 

  20. Muttil N, Chau K (2007) Machine learning paradigms for selecting ecologically significant input variable. Eng Appl Artif Intell 20(06):735–744. https://doi.org/10.1016/j.engappai.2006.11.016

    Article  Google Scholar 

  21. Muttil N, Chau K (2006) Neural network and genetic programming for modelling coastal algal blooms. Int J Environ Pollut 28(3–4):223–238. https://doi.org/10.1504/IJEP.2006.011208

    Article  Google Scholar 

  22. Muttil N, Lee JHW (2005) Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol Model 189(03):363–376. https://doi.org/10.1016/j.ecolmodel.2005.03.018

    Article  Google Scholar 

  23. Muttil N, Lee JHW, Jayawardena AW (2004) Real-time prediction of coastal algal blooms using genetic programming. In: 6th international conference on hydro informatics. Singapore, pp 890–897. https://doi.org/10.1142/9789812702838_0110

  24. Najah A, Elshafie A, Karim OA, Jaffar O (2009) Prediction of johor river water quality parameters using artificial neural networks. Eur J Sci Res 28(3):422–435

    Google Scholar 

  25. Nordin JP (1997). Evolutionary program induction of binary machine code and its application. Ph.D. dissertation, Department of Computer Science, University of Dortmund

    Google Scholar 

  26. Palani S, Liong S-Y, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597

    Article  Google Scholar 

  27. Preis A, Ostfeld A (2008) A coupled model tree–genetic algorithm scheme for flow and water quality predictions in watersheds. J Hydrol 349:364–375

    Article  Google Scholar 

  28. Recknagel F, Cao H, Kim B, Takamura N, Welk A (2006) Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation. Ecol Inform 1(2):133-151

    Google Scholar 

  29. Sawant R (2015). A comprehensive study of polluted river stretches and preparation of action plan of river Godavari from Nashik downstream to Paithan. The report, Aavanira Biotech P. Ltd., Maharashtra Pollution Control Board. http://mpcb.gov.in/ereports/pdf/GodavariRiver_ComprehensiveStudyReport.pdf

  30. Shiklomanov I (1993) Water in crisis: a guide to the world’s freshwater resources. In: Gleick PH (ed). Oxford University Press, New York, pp 13–25, https://www.academia.edu/902661/Water_in_Crisis_Chapter_2_Oxford_University_Press_1993

  31. Tikhe SS, Khare KC, Londhe SN (2015) Multicity seasonal air quality index forecasting using soft computing techniques. Adv Environ Res 4(02):83–104. https://doi.org/10.12989/aer.2015.4.2.083

  32. Shaikhina T, Khovanova NA (2017) Handling limited datasets with neural networks in medical applications: a small-data approach. Artif Intell Med 75:51–63. https://doi.org/10.1016/j.artmed.2016.12.003

  33. US Environmental Protection Agency (2009). Technical assistant document for reporting of daily air quality—air quality index. Research Triangle Park, North Carolina

    Google Scholar 

  34. Wang W, Chau K, Xu D, Chen X (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manage 29(08):2655–2675. https://doi.org/10.1007/s11269-015-0962-6

    Article  Google Scholar 

  35. Water Quality Criteria (2002) https://www.epa.gov/sites/production/files/2018-12/documents/national-recommended-hh-criteria-2002.pdf

  36. Whigham PA, Recknagel F (1999) Predictive modelling of plankton dynamics in freshwater lakes using genetic programming. The Information Science Discussion Paper Series. Department of Information Science, University of Otago, Dunedin, New Zealand, pp 1–7

    Google Scholar 

  37. Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

  38. Xiang, Y, Jiang L (2009) Water quality prediction using LS-SVM and particle swarm optimization. In: Conference proceedings of the second international workshop on knowledge discovery and data mining, WKDD 2009, Moscow, Russia. https://doi.org/10.1109/wkdd.2009.217

Download references

Author information

Authors and Affiliations

  1. SVC Polytechnique, Pune, Maharashtra, 411041, India

    Mrunalini Jadhav

  2. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, 412115, India

    Kanchan Khare, Sayali Apte & Rushikesh Kulkarni

Authors
  1. Mrunalini Jadhav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kanchan Khare
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sayali Apte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rushikesh Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kanchan Khare .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jadhav, M., Khare, K., Apte, S., Kulkarni, R. (2020). Predictive Analysis of Lake Water Quality Using an Evolutionary Algorithm. In: Kulkarni, A., Satapathy, S. (eds) Optimization in Machine Learning and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0994-0_5

Download citation

Publish with us

Policies and ethics

Search

Navigation