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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Environmental science and pollution research international Ročník 29; číslo 14; s. 20496 - 20516
Hlavní autori:	Alavi, Javad, Ewees, Ahmed A., Ansari, Sepideh, Shahid, Shamsuddin, Yaseen, Zaher Mundher
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!