A hybrid deep learning framework for predicting industrial wastewater influent quality based on graph optimisation-Reference-Cited by-同舟云学术

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A hybrid deep learning framework for predicting industrial wastewater influent quality based on graph optimisation

Published:2024-08 Issue: Volume:65 Page:105831
ISSN:2214-7144
Container-title:Journal of Water Process Engineering
language:en
Short-container-title:Journal of Water Process Engineering

Author:

Cao Jiafei,Xue Anke,Yang Yong,Lu Rongfeng,Hu Xiaojing,Zhang Le,Cao Wei,Cao Guanglong,Geng Xiulin,Wang Lin

Publisher

Elsevier BV

Reference47 articles.

1. Water quality modeling in sewer networks: review and future research directions;Jia;Water Res.,2021

2. Modeling and real-time control of urban drainage systems: a review;García;Adv. Water Resour.,2015

3. Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant;Matheri;Physics and Chemistry of the Earth, Parts A/B/C,2022

4. Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant;Ansari;Sci. Total Environ.,2020

5. A review of the artificial neural network models for water quality prediction;Chen;Appl. Sci.,2020

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