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


  • 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.


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


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.


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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