An integrated feature selection and hyperparameter optimization algorithm for balanced machine learning models predicting N2O emissions from wastewater treatment plants-Reference-Cited by-同舟云学术

An integrated feature selection and hyperparameter optimization algorithm for balanced machine learning models predicting N2O emissions from wastewater treatment plants

Published:2024-06 Issue: Volume:63 Page:105512
ISSN:2214-7144
Container-title:Journal of Water Process Engineering
language:en
Short-container-title:Journal of Water Process Engineering

Author:

Khalil Mostafa,AlSayed Ahmed,Liu Yang,Vanrolleghem Peter A.

Funder

NSERC

Canada Research Chairs

National Research Council Canada

Publisher

Elsevier BV

Reference45 articles.

1. An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants;Szeląg;J. Water Process Eng.,2023

2. Analyzing greenhouse gas emissions from municipal wastewater treatment plants using pollutants parameter normalizing method: a case study of Beijing;Zhou;J. Clean. Prod.,2022

3. Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return;Borzooei;Environ. Sci. Pollut. Res.,2020

4. Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions;Borzooei;Water Sci. Technol.,2020

5. Data mining application in assessment of weather-based influent scenarios for a WWTP: getting the most out of plant historical data;Borzooei;Water Air Soil Pollut.,2019

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