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Water quality index prediction with hybridized ELM and Gaussian process regression

Authors :
Wai Kok Poh
Koo Chai Hoon
Huang Yuk Feng
Chong Woon Chan
Source :
E3S Web of Conferences, Vol 347, p 04004 (2022)
Publication Year :
2022
Publisher :
EDP Sciences, 2022.

Abstract

The Department of Environment (DOE) of Malaysia evaluates river water quality based on the water quality index (WQI), which is a single number function that considers six parameters for its determination, namely the ammonia nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). The conventional WQI calculation is tedious and requires all parameter values in computing the final WQI. In this study, the extreme learning machine (ELM) and the radial basis function kernel Gaussian process regression (GPR), were enhanced with bootstrap aggregating (bagging) and adaptive boosting (AdaBoost) for the WQI prediction at the Klang River, Malaysia. The global performance indicator (GPI) was used to evaluate the models’ performance. By preparing different input combinations for the WQI prediction, the parameter importance was found in following order: DO > COD > SS > AN > BOD > pH, and all models demonstrated lower prediction accuracy with a lesser number of parameter inputs. The GPR revealed a consistent trend with higher WQI prediction accuracy than ELM. The Adaboost-ELM works better than the bagged-ELM for all input combinations, while the bagging algorithm improved the GPR prediction under certain scenarios. The bagged-GPR reported the highest GPI of 1.86 for WQI prediction using all six parameter inputs.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
347
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.f5d183fec6c84ae5ae4e4e762e28a694
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/202234704004