Universal pooling is a channel-wise attention-based trainable pooling layer that generalizes existing fixed pooling operations and outperforms them on two benchmark problems by adapting to the data.
Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model
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abstract
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 S{\o}ren 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).
fields
cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Universal Pooling -- A New Pooling Method for Convolutional Neural Networks
Universal pooling is a channel-wise attention-based trainable pooling layer that generalizes existing fixed pooling operations and outperforms them on two benchmark problems by adapting to the data.