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.
| Original language | English |
|---|---|
| Article number | 6254 |
| Number of pages | 15 |
| Journal | Sustainability |
| Volume | 12 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - Aug 2020 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Inflow and Infiltration (I/I)
- Adaptive Neuro-Fuzzy Inference System (ANFIS)
- Multilayer Perceptron Neural Network (MLPNN)
- sanitary sewer system
- adjusted weather-radar-rainfall data
- Artificial Intelligence (AI)
- GROUNDWATER INFILTRATION
- NEURAL-NETWORKS
- WATER-LEVEL
- FLOW DATA
- PREDICTION
- RAINFALL
- SYSTEMS
- IMPROVE
- RADAR
