Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis - A Data Driven Approach

Authors

  • Y. Khan Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia.
  • S.S. Chai Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia.

Keywords:

Water Quality Prediction, Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Ensemble Learning, Machine Learning,

Abstract

The consequences of un-clean water are some of the direst issues faced by humanity today. These concerns can be addressed efficiently if data is pre-analyzed and water quality is predicted before its effects occur. The aim of this research is to develop a novel ensemble of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using averaging ensemble technique, producing improved prediction accuracy. Measurements of different water quality parameters have been used for predicting the overall water quality, applying ANN, ANFIS and ANN-ANFIS ensemble and their results have been compared. The data used in this study is obtained by USGS online repository for the year of 2015, with a 30-minutes time interval between measurements. Root Mean Squared Error (RMSE) has been used as the main performance measure. The results depict a significant improvement in the Ensemble ANN-ANFIS model (RMSE: 0.457) as compared to both the ANN model (RMSE: 2.709) and the ANFIS model (1.734). The study concludes that the ensemble of ANN and ANFIS model shows significant improvement in prediction performance as compared to the individual models. The research can prove to be beneficial for decision making in terms of water quality improvement.

References

P. Zeilhofer, “GIS applications for mapping and spatial modeling of urban-use water quality: a case study in District of Cuiabá, Mato Grosso, Brazil,” Cad. Saúde …, vol. 23, no. 4, pp. 875–884, 2007.

N. M. Gazzaz, M. K. Yusoff, A. Z. Aris, H. Juahir, and M. F. Ramli, “Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors,” Mar. Pollut. Bull., vol. 64, no. 11, pp. 2409–2420, 2012.

Y. Khan and C. Soo See, “Predicting and Analyzing Water Quality using Machine Learning : A Comprehensive Model,” in 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2016, pp. 1–6.

Y. Wang, Y. Wang, M. Ran, Y. Liu, Z. Zhang, L. Guo, Y. Zhao, and P. Wang, “Identifying Potential Pollution Sources in River Basin via Water Quality Reasoning Based Expert System,” 2013 Fourth Int. Conf. Digit. Manuf. Autom., pp. 671–674, 2013.

A. Tizro, M. Ghashghaie, P. Georgiou, and K. Voudouris, “A r w w,” vol. 1, pp. 43–52, 2014.

C. N. Babu and B. E. Reddy, “A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data,” Appl. Soft Comput., vol. 23, no. January 2016, pp. 27–38, 2014.

Y. Park, K. H. Cho, J. Park, S. M. Cha, and J. H. Kim, “Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.,” Sci. Total Environ., vol. 502, pp. 31–41, Jan. 2015.

C. Min, “An Improved Recurrent Support Vector Regression Algorithm for Water Quality Prediction,” vol. 12, pp. 4455–4462, 2011.

S. E. Kim and I. W. Seo, “Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers,” J. Hydro-environment Res., Apr. 2015.

S. Maiti and R. K. Tiwari, “A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction,” Environ. Earth Sci., vol. 71, no. 7, pp. 3147–3160, 2013.

Y. Gong, Y. Zhang, S. Lan, and H. Wang, “A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida,” Water Resour. Manag., pp. 375–391, 2015.

O. Baghirli, “Comparison of Lavenberg-Marquardt, Scaled Conjugate Gradient And Bayesian Regularization Backpropagation Algorithms for Multistep Ahead Wind Speed Forecasting Using Multilayer Perceptron Feedforward Neural Network,” Dissertation, no. June, p. Uppsala University, 2015.

A. Rahimzadeh, F. Z. Ashtiani, and A. Okhovat, “Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume,” J. Environ. Chem. Eng., vol. 4, no. 1, pp. 576–584, 2016.

M. Talebizadeh and A. Moridnejad, “Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models,” Expert Syst. Appl., vol. 38, no. 4, pp. 4126–4135, 2011.

A. A. M. Ahmed and S. M. A. Shah, “Application of adaptive neurofuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River,” J. King Saud Univ. - Eng. Sci., p. , 2015.

T. Taskaya-Temizel and M. C. Casey, “A comparative study of autoregressive neural network hybrids,” Neural Networks, vol. 18, no. 5–6, pp. 781–789, 2005.

H. Daume, “Ensemble Methods,” in A course in machine learning, 2012, p. 189.

S. Barak and S. S. Sadegh, “Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm,” Int. J. Electr. Power Energy Syst., vol. 82, pp. 92–104, 2016.

S. Nagi and D. K. Bhattacharyya, “Classification of microarray cancer data using ensemble approach,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 2, no. 3, pp. 159–173, 2013.

The Environmental and Protection Agency, “Parameters of water quality,” Environ. Prot., p. 133, 2001.

M. Wills and K. N. Irvine, “Application of the National Sanitation Foundation Water Quality Index in the cazenovia Creek, MY, Pilot Watershed Management Project,” Middle States Geogr., pp. 95–104, 1996.

H. Juahir, M. A. Zali, A. Retnam, S. M. Zain, M. F. Kasim, B. Abdullah, and S. B. Saadudin, “Sensitivity analysis for water quality index (WQI) prediction for kinta river, Malaysia,” World Appl. Sci. J., vol. 14, no. SPL ISS 1, pp. 60–65, 2011.

N. Snchez-Marono and A. Alonso-Betanzos, Feature selection based on sensitivity analysis. 2007.

H. Yan, Z. H. Zou, and H. W. Wang, “Adaptive neuro fuzzy inference system for classification of water quality status,” J. Environ. Sci., vol. 22, no. 12, pp. 1891–1896, 2010.

C. Loganathan and K. V Girija, “Hybrid Learning For Adaptive Neuro Fuzzy Inference System,” vol. 2, no. 11, pp. 6–13, 2013.

C. Loganathan and K. V Girija, “Investigations on Hybrid Learning in ANFIS,” Int. J. Eng. Res. Appl., vol. 4, no. 10, pp. 31–37, 2014.

P. Kazienko, E. Lughofer, and B. Trawiński, “Hybrid and ensemble methods in machine learning J.UCS special issue,” J. Univers. Comput. Sci., vol. 19, no. 4, pp. 457–461, 2013.

D. R. Legates and G. J. McCabe Jr., “Evaluating the Use of ‘Goodness of Fit’ Measures in Hydrologic and Hydroclimatic Model Validation,” Water Resour. Res., vol. 35, no. 1, pp. 233–241, 1999.

C. Willmott, “Some comments on the evaluation of model performance,” Bulletin of the American Meteorological Society, vol. 63, no. 11. pp. 1309–1313, 1982.

Downloads

Published

2017-09-15

How to Cite

Khan, Y., & Chai, S. (2017). Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis - A Data Driven Approach. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-9), 117–122. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2685