Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP-Reference-Cited by-同舟云学术

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Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP

Published:2024-04-02 Issue:4 Volume:4 Page:1904-1915
ISSN:2690-0637
Container-title:ACS ES&T Water
language:en
Short-container-title:ACS EST Water

Author:

Li Ruojia¹,Feng Kuanliang²,An Tong¹,Cheng Peijin¹,Wei Lecheng¹,Zhao Zihao³,Xu Xiangyang¹⁴,Zhu Liang¹⁴⁵^ORCID

Affiliation:

1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

2. Zhejiang Supcon Information Co., Ltd, Hangzhou 310052, China

3. Shanghai Municipal Engineering Design Institute (group) Co., Ltd, Shanghai 200092, China

4. Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou 310058, China

5. Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan 314100. China

Funder

Major Scientific and Technological Project of Zhejiang Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsestwater.4c00040

Reference56 articles.

1. Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent

2. Sliding window neural network based sensing of bacteria in wastewater treatment plants

3. Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods

4. Country-level and gridded estimates of wastewater production, collection, treatment and reuse

5. Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration

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