Affiliation:
1. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan 65181-15743, Iran
2. Research Center of Environmental Health Technology, Iran University of Medical Sciences, Tehran 14496-14535, Iran
3. Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran
4. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Republic of Korea
5. Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda 21000, Algeria
6. Department of Civil Engineering, Luebeck University of Applied Sciences, 23562 Lübeck, Germany
7. Department of Civil Engineering, Ilia State University, 0162 Tbilisi, Georgia
8. Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea
Abstract
The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.
Funder
Iran University of Medical Sciences
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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