A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 29; H. 14; S. 20496 - 20516
Hauptverfasser:	Alavi, Javad, Ewees, Ahmed A., Ansari, Sepideh, Shahid, Shamsuddin, Yaseen, Zaher Mundher
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Schlagworte:	Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution chemical oxygen demand Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Machine Learning prediction Research Article swarms time series analysis Waste Water Technology Wastewater wastewater treatment Water Management Water Pollution Control Real-time water quality prediction Time-series learning Kernel-based extreme learning machine Wastewater Consumer behaviour Intelligent algorithms
ISSN:	0944-1344, 1614-7499, 1614-7499
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Abstract	Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
AbstractList	Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044. Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044. Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
Author	Yaseen, Zaher Mundher Ewees, Ahmed A. Alavi, Javad Shahid, Shamsuddin Ansari, Sepideh
Author_xml	– sequence: 1 givenname: Javad surname: Alavi fullname: Alavi, Javad organization: Department of Environmental Sciences and Engineering, Kheradgarayan Motahar Institute of Higher Education – sequence: 2 givenname: Ahmed A. surname: Ewees fullname: Ewees, Ahmed A. organization: Computer Department, Damietta University – sequence: 3 givenname: Sepideh surname: Ansari fullname: Ansari, Sepideh organization: Department of Environmental Sciences and Engineering, Kheradgarayan Motahar Institute of Higher Education – sequence: 4 givenname: Shamsuddin surname: Shahid fullname: Shahid, Shamsuddin organization: School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM) – sequence: 5 givenname: Zaher Mundher orcidid: 0000-0003-3647-7137 surname: Yaseen fullname: Yaseen, Zaher Mundher email: zaheryaseen88@gmail.com, yaseen@alayen.edu.iq organization: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, College of Creative Design, Asia University
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Keywords	Real-time water quality prediction Time-series learning Kernel-based extreme learning machine Wastewater Consumer behaviour Intelligent algorithms
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Snippet	Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the... Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the...
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SubjectTerms	Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution chemical oxygen demand Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Machine Learning prediction Research Article swarms time series analysis Waste Water Technology Wastewater wastewater treatment Water Management Water Pollution Control
Title	A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms
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