Neural Computing and Applications

, Volume 31, Issue 12, pp 8393–8409 | Cite as

Fecal coliform predictive model using genetic algorithm-based radial basis function neural networks (GA-RBFNNs)

  • Sai Prasanth Duvvuri
  • Jagadeesh AnmalaEmail author
Original Article


A genetic algorithm (GA)-based water quality model is built to predict fecal coliform from five independent factors, namely two-day cumulative precipitation, temperature, urban, forest, and agricultural land use factors. The current work succeeds the previous work of the corresponding author which studied the relationship between fecal coliform and the five factors using artificial neural networks and GIS for a watershed consisting of Green River in Kentucky, USA. In the current paper, the main idea is to develop the hyper-parameters (center and radius) of the radial basis function (RBF) neural network from a GA. A GA is one of the nature-inspired evolutionary algorithms, which is an iterative one and successful in solving many optimization problems. Genetic algorithms are generally used for finding the maxima or minima from a given set of population members. They can also be used to tune the hyper-parameters which are found in the current paper for application in water quality (or fecal coliform) modeling using RBF neural networks. The predicted result using GA-based RBF neural networks is found to be slightly better than without using GAs.


Radial basis function neural networks Genetic algorithms Water quality model Application of AI methods Fecal coliform model Land use factors 



The authors would like to thank Mr. Tim Rink (GIS Analyst), Jenna Harbaugh (GIS Analyst), Dr. Stuart Foster, Director of Kentucky Climate Center, Dr. Ouida Meier, and Prof. Albert J. Meier of Western Kentucky University for helping us with the required data.


  1. 1.
    Anmala J, Ouida WM, Albert JM, Grubbs S (2015) GIS and artificial neural network-based water quality model for a stream network in the Upper Green River Basin, Kentucky, USA. J Environ Eng 141:04014082-1–04014082-15. CrossRefGoogle Scholar
  2. 2.
    ASCE Task Committee Paper I (2000) Artificial neural networks in hydrology: preliminary concepts. J Hydrol Eng 5(2):115–123CrossRefGoogle Scholar
  3. 3.
    Aras E, Togan V, Berkun M (2007) River water quality management model using genetic algorithm. Environ Fluid Mech 7:439–450CrossRefGoogle Scholar
  4. 4.
    Arora Y, Singhal A, Bansal A (2014) A study of applications of RBF network. Int J Comput Appl 94(2):17–20Google Scholar
  5. 5.
    Bagheri M, Mirbagheri SA, Bagheri Z, Kamarkhani AM (2015) Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach. Process Saf Environ Prot. CrossRefGoogle Scholar
  6. 6.
    Chang C-C, Chen S-H (2015) A comparative analysis on artificial neural network-based two-stage clustering. Cogent Eng 2:995785CrossRefGoogle Scholar
  7. 7.
    Chang F-J, Chen L, Chang L-C (2005) Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol Process 19:2277–2289CrossRefGoogle Scholar
  8. 8.
    Haykin, S (2005), Neural Networks A Comprehensive Foundation, Pearson Education, IncGoogle Scholar
  9. 9.
    Kumar DN, Raju KS, Ashok B (2006) Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. J Irrig Drain Eng 132(2):123–129CrossRefGoogle Scholar
  10. 10.
    McDonald JH (2009) Handbook of biological statistics, 2nd edn. Sparky House, BaltimoreGoogle Scholar
  11. 11.
    Mor M, Gupta P, Sharma P (2014) A genetic algorithm approach for clustering. Int J Eng Comput Sci 3(6):6442–6447Google Scholar
  12. 12.
    Prasad TD, Park N-S (2004) Multiobjective genetic algorithms for design of water distribution systems. J Water Resour Plan Manag 130(1):73–82CrossRefGoogle Scholar
  13. 13.
    Ranaweera DK, Hubele NF, Papalexopoulos AD (1995) Application of radial basis function neural network model for short-term load forecasting. IEE Proc Gener Transm Distrib 142(1):45–50CrossRefGoogle Scholar
  14. 14.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Müller A, Nothman J, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in python. JMLR 12:2825–2830MathSciNetzbMATHGoogle Scholar
  15. 15.
    Sharif M, Wardaw R (2000) Multireservoir systems optimization using genetic algorithms: case study. J Comput Civ Eng 14(4):255–263CrossRefGoogle Scholar
  16. 16.
    Shanthi Rani MM, Chitra P (2016) Region of interest based compression of medical images using vector quantization. Int J Comput Sci Inf Technol 4(1):29–37Google Scholar
  17. 17.
    Sokal RR, Rohlf JF (1981) Biometry: the principle and practice of statistics in biological research, 2nd edn. W.H. Freeman and Company, San FranciscozbMATHGoogle Scholar
  18. 18.
    Tolson BA, Maier HR, Simpson AR, Lence BJ (2004) Genetic algorithms for reliability-based optimization of water distribution systems. J Water Resour Plan Manag 130(1):63–72CrossRefGoogle Scholar
  19. 19.
    Tufail M, Ormsbee L, Teegavarapu R (2008) Artificial intelligence-based inductive models for prediction and classification of fecal coliform in surface waters. J Environ Eng 134(9):789–799CrossRefGoogle Scholar
  20. 20.
    Uppada SK (2014) Centroid based clustering algorithms—a clarion study. Int J Comput Sci Inf Technol 5(6):7309–7313Google Scholar
  21. 21.
    Vairavamoorthy K, Ali M (2000) Optimal design of water distribution systems using genetic algorithms. Comput-Aided Civ Infrastruct Eng 15:374–382CrossRefGoogle Scholar
  22. 22.
    Valory JPL, Reis JAT, Mendonca ASF (2016) Combining genetic algorithms with a water quality model to determine efficiencies of sewage treatment systems in watersheds. J Environ Eng 142(3):04015080-1–04015080-9Google Scholar
  23. 23.
    Wang Z, Shao D, Yang H, Yang S (2015) Prediction of water quality in south to north water transfer project of China based on GA-optimized general regression neural network. Water Sci Technol: Water Supply 15(1):150–157Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information ScienceBirla Institute of Technology and Science, PilaniHyderabadIndia
  2. 2.Department of Civil EngineeringBirla Institute of Technology and Science, PilaniHyderabadIndia

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