Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence
In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. Water quality and operational...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
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Series: | Water |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4441/14/15/2423 |