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.
Highlights
Sewer systems are used to collect sewage from water consumers and convey it to wastewater-treatment plants, forming part of society’s critical infrastructure
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
In the Adaptive Neuro-Fuzzy Inference System (ANFIS) model, a parameterized model structure of membership functions and rules were generated, and eight Gaussian MFs were created for each training process
Summary
Sewer systems are used to collect sewage from water consumers and convey it to wastewater-treatment plants, forming part of society’s critical infrastructure. Dry weather flow should follow the same pattern as water consumption in a physically undamaged, watertight network. This pattern reaches its minimum at night and has two peaks during the day [1]. In modern installations, pumping stations are connected to a control-room environment through the supervisory control and data acquisition (SCADA) system that automatically transfers flow data to the database system. Extraneous flow resulting from inflow and infiltration (I/I) is a commonly experienced problem in sewer systems. A high I/I level can be an important indicator of the operational and structural condition of a sewer network [2,3]. Infiltration indicates that pipes and manholes have structural deficiencies, whereas inflow implies inadequate runoff management or deficient manhole covers. The identification of I/I sources requires costly and laborious inspections and measurement campaigns, and results are subject to uncertainty
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