Sungwon Kim1, Meysam Alizamir2, Mohammad Zounemat-Kermani3, Ozgur Kisi4, Vijay P Singh5. 1. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea. Electronic address: swkim1968@dyu.ac.kr. 2. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran. Electronic address: meysamalizamir@gmail.com. 3. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Electronic address: zounemat@uk.ac.ir. 4. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia. Electronic address: ozgur.kisi@iliauni.edu.ge. 5. Distinguished Professor and Caroline & William N. Lehrer Distinguished Chair in Water Engineering, Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, 77843-2117, USA; National Water Center, UAE University, Al Ain, United Arab Emirates. Electronic address: vsingh@tamu.edu.
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 (R2), 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.
The biochemical oxygen demand (BOD), one of widely utilized variables for n>an class="Chemical">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 (R2), 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.
Keywords:
Biochemical oxygen demand; Deep echo state network; Extreme learning machine; Gradient boosting regression tree; Random forests; Water quality prediction
Authors: Romeo Victor Ionescu; Valentin Marian Antohi; Monica Laura Zlati; Lucian Puiu Georgescu; Catalina Iticescu Journal: Int J Environ Res Public Health Date: 2022-09-29 Impact factor: 4.614