Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Journal: Journal of environmental management
Published Date: Oct 7, 2021

Abstract

Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L for NH-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.

Authors

  • Xuan Cuong Nguyen
    Laboratory of Energy and Environmental Science, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.
  • Thi Thanh Huyen Nguyen
    Laboratory of Energy and Environmental Science, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.
  • Quyet V Le
    Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul 02841, Republic of Korea.
  • Phuoc Cuong Le
    Department of Environmental Management, Faculty of Environment, The University of Danang-University of Science and Technology, Danang, 550000, Viet Nam.
  • Arun Lal Srivastav
    Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.
  • Quoc Bao Pham
    Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
  • Phuong Minh Nguyen
    Faculty of Environmental Sciences, University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam.
  • D Duong La
    Institute of Chemistry and Materials, Nghia Do, Cau Giay, Hanoi, Viet Nam.
  • Eldon R Rene
    Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga, 10, E-15008 La Coruña, Spain; Department of Environmental Engineering and Water Technology, UNESCO-IHE, P.O. Box 3015, 2601 DA Delft, The Netherlands.
  • H Hao Ngo
    Center for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Australia.
  • S Woong Chang
    Department of Environmental Energy Engineering, Kyonggi University, Suwon 442-760, Republic of Korea.
  • D Duc Nguyen
    Faculty of Environmental and Food Engineering, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh, District 4, Ho Chi Minh City, 755414, Viet Nam; Department of Environmental Energy Engineering, Kyonggi University, Suwon 442-760, Republic of Korea. Electronic address: nguyensyduc@gmail.com.

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