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Article

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

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Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX 77843, USA
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Department of Built Environment, Aalto University, Otakari 4, 00076 Espoo, Finland
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Department of Electrical & Computer Engineering, Texas A&M University, 3127 TAMU, College Station, TX 77843, USA
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Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
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Hydraulic Engineering Institute, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China
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Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Mathews Ave, Urbana, IL 61801, USA
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Texas A&M AgriLife Center at El Paso, Texas A&M University, 1380 A&M Circle, El Paso, TX 79927, USA
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(15), 6254; https://doi.org/10.3390/su12156254
Received: 7 June 2020 / Revised: 26 July 2020 / Accepted: 31 July 2020 / Published: 3 August 2020
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. View Full-Text
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) 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)
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MDPI and ACS Style

Zhang, Z.; Laakso, T.; Wang, Z.; Pulkkinen, S.; Ahopelto, S.; Virrantaus, K.; Li, Y.; Cai, X.; Zhang, C.; Vahala, R.; Sheng, Z. Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments. Sustainability 2020, 12, 6254. https://doi.org/10.3390/su12156254

AMA Style

Zhang Z, Laakso T, Wang Z, Pulkkinen S, Ahopelto S, Virrantaus K, Li Y, Cai X, Zhang C, Vahala R, Sheng Z. Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments. Sustainability. 2020; 12(15):6254. https://doi.org/10.3390/su12156254

Chicago/Turabian Style

Zhang, Zhe, Tuija Laakso, Zeyu Wang, Seppo Pulkkinen, Suvi Ahopelto, Kirsi Virrantaus, Yu Li, Ximing Cai, Chi Zhang, Riku Vahala, and Zhuping Sheng. 2020. "Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments" Sustainability 12, no. 15: 6254. https://doi.org/10.3390/su12156254

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