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A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

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  • Angela Gorgoglione

    (Department of Fluid Mechanics and Environmental Engineering, School of Engineering, Universidad de la República, Montevideo 11300, Uruguay
    Equally contributed to the paper.)

  • Alberto Castro

    (Department of Computer Science, School of Engineering, Universidad de la República, Montevideo 11300, Uruguay
    Department of Electrical Engineering, School of Engineering, Universidad de la República, Montevideo 11300, Uruguay
    Equally contributed to the paper.)

  • Vito Iacobellis

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry, Politecnico di Bari, 70126 Bari, Italy)

  • Andrea Gioia

    (Department of Civil, Environmental, Land, Building Engineering and Chemistry, Politecnico di Bari, 70126 Bari, Italy)

Abstract

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

Suggested Citation

  • Angela Gorgoglione & Alberto Castro & Vito Iacobellis & Andrea Gioia, 2021. "A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2054-:d:499320
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    References listed on IDEAS

    as
    1. Deepak Singh Bisht & Chandranath Chatterjee & Shivani Kalakoti & Pawan Upadhyay & Manaswinee Sahoo & Ambarnil Panda, 2016. "Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 749-776, November.
    2. Maria Di Modugno & Andrea Gioia & Angela Gorgoglione & Vito Iacobellis & Giovanni La Forgia & Alberto F. Piccinni & Ezio Ranieri, 2015. "Build-Up/Wash-Off Monitoring and Assessment for Sustainable Management of First Flush in an Urban Area," Sustainability, MDPI, vol. 7(5), pages 1-21, April.
    3. Angela Gorgoglione & Andrea Gioia & Vito Iacobellis, 2019. "A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    4. Lilian Ding & Qiyao Li & Jianjun Tang & Jiangfei Wang & Xin Chen, 2019. "Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
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    Cited by:

    1. Byungwoong Choi & Seung Se Choi, 2021. "Integrated Hydraulic Modelling, Water Quality Modelling and Habitat Assessment for Sustainable Water Management: A Case Study of the Anyang-Cheon Stream, Korea," Sustainability, MDPI, vol. 13(8), pages 1-16, April.

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