Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant-Reference-Cited by-同舟云学术

Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant

Published:2020-06 Issue: Volume:722 Page:137878
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Ansari Mozafar^ORCID,Othman Faridah^ORCID,El-Shafie Ahmed^ORCID

Funder

Ministry of Education

University of Malaya

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference77 articles.

1. Towards the real-time monitoring of industrial wastewater treatment processes via photoelectrochemical oxygen demand measurements;Aghasadeghi;Environmental Science: Water Research & Technology,2018

2. Optimisation of reverse osmosis based wastewater treatment system for the removal of chlorophenol using genetic algorithms;Al-Obaidi;Chem. Eng. J.,2017

3. Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia;Ansari;Environ. Sci. Pollut. Res.,2018

4. Evaluating the efficiency of artificial intelligence models to predict missing water quality parameter data for a wastewater treatment plant;Ansari,2018

5. Wastewater treatment aeration process optimization: a data mining approach;Asadi;J. Environ. Manag.,2017

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis;Journal of Water Process Engineering;2024-02

2. Exploring ANFIS application based on actual data from wastewater treatment plant for predicting effluent removal quality of selected major pollutants;Journal of Water Process Engineering;2023-12

3. Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge;Sensors;2023-10-19

4. Performance evaluation of ANN and ANFIS models for estimating velocity and pressure in water distribution networks;Water Supply;2023-08-24

5. Convolutional neural network–multi-kernel radial basis function neural network–salp swarm algorithm: a new machine learning model for predicting effluent quality parameters;Environmental Science and Pollution Research;2023-08-23