Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence

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...

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Bibliographic Details
Main Authors: Jungsu Park, Juahn Ahn, Junhyun Kim, Younghan Yoon, Jaehyeoung Park
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Water
Subjects:
ensemble model
explainable artificial intelligence
machine learning
water treatment system
XGBoost
Online Access:https://www.mdpi.com/2073-4441/14/15/2423

Internet

https://www.mdpi.com/2073-4441/14/15/2423

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