Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea. (15th September 2020)

• Record Type: Journal Article
• Authors: Kim, Sungwon; Alizamir, Meysam; Zounemat-Kermani, Mohammad; Kisi, Ozgur; Singh, Vijay P.
• Is Part Of: Journal of environmental management. Volume 270(2020)
• Publication Date: 2020-09-15
• Subjects: Biochemical oxygen demand -- Deep echo state network -- Extreme learning machine -- Gradient boosting regression tree -- Random forests -- Water quality prediction; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.110834
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1–5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R 2 ), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations. Highlights: Machine learning models are developed for predicting the biochemical oxygen demand. Water quality variables are used for model development based on input categories 1–5. Four statistical indices are utilized for evaluating and assessing the developing models. Deep echo state network 5 model provides the most accurate results to predict BOD.