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

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.

[1]  Li Chen,et al.  Optimizing the reservoir operating rule curves by genetic algorithms , 2005 .

[2]  Abhishek Singhal,et al.  A Study of Applications of RBF Network , 2014 .

[3]  R. Sokal,et al.  Biometry: The Principles and Practice of Statistics in Biological Research (2nd ed.). , 1982 .

[4]  Poonam Gupta,et al.  A Genetic Algorithm Approach for Clustering , 2014 .

[5]  Robin Wardlaw,et al.  Multireservoir Systems Optimization Using Genetic Algorithms: Case Study , 2000 .

[6]  Majid Bagheri,et al.  Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach , 2015 .

[7]  Mohammed Ali,et al.  Optimal Design of Water Distribution Systems Using Genetic Algorithms , 2000 .

[8]  D. K. Ranaweera,et al.  Application of radial basis function neural network model for short-term load forecasting , 1995 .

[9]  Lindell Ormsbee,et al.  Artificial Intelligence-Based Inductive Models for Prediction and Classification of Fecal Coliform in Surface Waters , 2008 .

[10]  Jagadeesh Anmala,et al.  GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA , 2015 .

[11]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[12]  Angus R. Simpson,et al.  Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems , 2004 .

[13]  José Antonio Tosta dos Reis,et al.  Combining Genetic Algorithms with a Water Quality Model to Determine Efficiencies of Sewage Treatment Systems in Watersheds , 2016 .

[14]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[15]  T. Devi Prasad,et al.  Multiobjective Genetic Algorithms for Design of Water Distribution Networks , 2004 .

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Dongguo Shao,et al.  Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network , 2015 .

[18]  K. Srinivasa Raju,et al.  Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms , 2006 .

[19]  Santosh Kumar Uppada.  Centroid Based Clustering Algorithms-A Clarion Study , 2014 .

[20]  Ssu-Han Chen,et al.  A comparative analysis on artificial neural network-based two-stage clustering , 2015 .

[21]  Vedat Toğan,et al.  River water quality management model using genetic algorithm , 2007 .

[22]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..