2018/11/09 by Duo Zhang, Erlend Skullestad Hølland, Zhang, Duo +6 · 5 citations
Computer Science · Engineering · Environmental Science · Mathematics · #Artificial intelligence #Artificial neural network #Civil engineering #Computer science #Computers and Society (cs.CY) #Construct (python library) #Engineering #Environmental engineering #FOS: Computer and information sciences #Flood Risk Assessment and Management #Hydrology and Watershed Management Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Recurrent neural network #Sewage treatment #Transfer of learning #Wastewater #Water Systems and Optimization #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.06367
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/11/09 · openalex publication_date 2018/11/09 · arxiv updated 2018/11/16 · openalex created_date 2018/11/29 · openalex updated_date 2026/07/28
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the effectiveness of ICWT is investigated in a sewer system in Drammen, Norway. Concerning the whole system performance, we found that the Søren Lemmich pump station plays a vital role in the ICWT framework. To enhance the operation of this pump station, it is imperative to construct a multi-step ahead water level prediction model. Hence, one of the most promising artificial intelligence techniques, Long Short Term Memory (LSTM), is employed to undertake this task. Experiments demonstrated that LSTM is superior to Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural Network (FFNN) and Support Vector Regression (SVR).