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

Zhe Zhang, Tuija Laakso, Zeyu Wang, Seppo Pulkkinen, Suvi Ahopelto, Kirsi Virrantaus, Yu Li, Ximing Cai, Chi Zhang, Riku Vahala, Zhuping Sheng

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number6254
Issue number15
StatePublished - Aug 3 2020


  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Adjusted weather-radar-rainfall data
  • Artificial intelligence (AI)
  • Inflow and infiltration (I/I)
  • Multilayer perceptron neural network (MLPNN)
  • Sanitary sewer system

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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