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

Khan, Yafra and Chai, S.S. (2017) Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis - A Data Driven Approach. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-9). p. 117. ISSN 2289-8131

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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.

Item Type: Article
Additional Information: Information, Communication and Creative Technology
Uncontrolled Keywords: Water Quality Prediction, Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System; Ensemble Learning; Machine Learning, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak.
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology
Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 08 Oct 2018 02:04
Last Modified: 29 Sep 2022 03:23
URI: http://ir.unimas.my/id/eprint/21937

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