Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)

Senlin Zhu,Salim Heddam

doi:10.2166/wqrj.2019.053

Abstract

Abstract In the present study, two non-linear mathematical modelling approaches, namely, extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) were developed to predict daily dissolved oxygen (DO) concentrations. Water quality data from four urban rivers in the backwater zone of the Three Gorges Reservoir, China were used. The water quality data selected consisted of daily observed water temperature, pH, permanganate index, ammonia nitrogen, electrical conductivity, chemical oxygen demand, total nitrogen, total phosphorus and DO. The accuracy of the ELM model was compared with the standard MLPNN using several error statistics such as root mean squared error, mean absolute error, the coefficient of correlation and the Willmott index of agreement. Results showed that the ELM and MLPNN models perform well for the Wubu River, acceptably for the Yipin River and moderately for the Huaxi River, while poor model performance was obtained at the Tributary of Huaxi River. Model performance is negatively correlated with pollution level in each river. The MLPNN model slightly outperforms the ELM model in DO prediction. Overall, it can be concluded that MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

Highlights

Dissolved oxygen (DO) is an essential resource of aquatic ecosystems
Results obtained show that the extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) models perform well for the Wubu River, acceptably for the Yipin River, moderately for the Huaxi River, and poorly for the Tributary of Huaxi River in DO prediction. This can be explained by considering that: (i) model performance is negatively correlated with pollution level in each river, (ii) the MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers
It was observed that the best accuracy obtained using MLPNN and ELM models differs widely from river to river, and it is sometimes difficult to select the best architecture among the nine input combinations

Summary

Introduction

The DO concentration plays a critical role in regulating various biogeochemical processes and biological communities in rivers. DO concentrations can fluctuate over the day and night in response to climate changes and the respiratory requirements of aquatic plants (Heddam a). Severe oxygen depletion can lead to fish kills (Meding & Jackson ; Robarts et al ), and changes in. The overall DO concentrations in a river are balanced by re-aeration at the water surface, primary production by photosynthesis and consumptions by biochemical oxygen demands in the water column or sediment oxygen demand (Poulson & Sullivan )

Objectives

Methods

Results

Conclusion