20200407_Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model
1. Title, Journal, and Authors
Title: Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model
Authors: Yafra Khan, Chai Soo See
* Faculty of Computer Science and Information Technology University Malaysia Sarawak Kota Samarahan, Malaysia
This research is carried to devise a comprehensive water prediction model using an artificial intelligence model (ANN), using multivariate statistical techniques such as PCA, and Non-linear Autoregressive (NAR) model. The historical data of water quality with a 6-minutes time interval for 5 months for 4 parameters (DO, Specific Conductance, Chlorophyll, and DO) are used to input data.
In the results of modeling, though all the parameters show high prediction accuracies, in the case of specific conductance, there is overfitting for the training data. Therefore, the adequate balance between bias and variance is needed to improve this model, afterward.
3. Application to research
- Time-series data analysis will be needed to predict the future.
Dae Seong Jeong / Integrated Ph.D. program
Environmental Systems Engineering Lab.
School of Environmental Science & Engineering
Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
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