IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p6254-d394006.html
   My bibliography  Save this article

Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments

Author

Listed:
  • Zhe Zhang

    (Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX 77843, USA)

  • Tuija Laakso

    (Department of Built Environment, Aalto University, Otakari 4, 00076 Espoo, Finland)

  • Zeyu Wang

    (Department of Electrical & Computer Engineering, Texas A&M University, 3127 TAMU, College Station, TX 77843, USA)

  • Seppo Pulkkinen

    (Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland)

  • Suvi Ahopelto

    (Department of Built Environment, Aalto University, Otakari 4, 00076 Espoo, Finland)

  • Kirsi Virrantaus

    (Department of Built Environment, Aalto University, Otakari 4, 00076 Espoo, Finland)

  • Yu Li

    (Hydraulic Engineering Institute, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China)

  • Ximing Cai

    (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Mathews Ave, Urbana, IL 61801, USA)

  • Chi Zhang

    (Hydraulic Engineering Institute, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China)

  • Riku Vahala

    (Department of Built Environment, Aalto University, Otakari 4, 00076 Espoo, Finland)

  • Zhuping Sheng

    (Texas A&M AgriLife Center at El Paso, Texas A&M University, 1380 A&M Circle, El Paso, TX 79927, USA)

Abstract

Inflow and infiltration (I/I) is a common problem in sanitary sewer systems. The I/I rate is also considered to be an important indicator of the operational and structural condition of the sewer system. Situation awareness in sanitary sewer systems requires accurate wastewater-flow information at a fine spatiotemporal scale. This study aims to develop artificial intelligence (AI)-based models (adaptive neurofuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN)) and to compare their performance for identifying the potential inflow and infiltration of the sanitary sewer subcatchment of two pumping stations. We tested the performance of these AI models by using data gathered from two pumping stations through a supervisory control and data acquisition (SCADA) system. As a result, these two AI models produced similar inflow and infiltration patterns—both subcatchments experienced inflow and infiltration. On the other hand, the ANFIS had overall higher performance than that of the MLPNN model for modelling the I/I situation for the catchments. The results of the research can be used to support spatial decision making in sewer system maintenance.

Suggested Citation

  • Zhe Zhang & Tuija Laakso & Zeyu Wang & Seppo Pulkkinen & Suvi Ahopelto & Kirsi Virrantaus & Yu Li & Ximing Cai & Chi Zhang & Riku Vahala & Zhuping Sheng, 2020. "Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments," Sustainability, MDPI, vol. 12(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6254-:d:394006
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/6254/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/15/6254/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6254-:d:394006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.