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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Vydáno v:Environmental science and pollution research international Ročník 29; číslo 14; s. 20496 - 20516
Hlavní autoři: Alavi, Javad, Ewees, Ahmed A., Ansari, Sepideh, Shahid, Shamsuddin, Yaseen, Zaher Mundher
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Témata:
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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Shrnutí:   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.
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ISSN:0944-1344
1614-7499
1614-7499
DOI:10.1007/s11356-021-17190-2