Water quality index prediction with hybridized ELM and Gaussian process regression

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.