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- W3109469733 abstract "Combined sewer overflows represent significant risks to human health as untreated water is discharged to the environment. Municipalities recently began collecting large amounts of water-related data and considering the adoption of deep learning solutions like recurrent neural network (RNN) for overflow prediction. In this paper, we contribute a novel metamorphic relation to characterize RNN robustness in the presence of missing data. We show how this relation drives automated testing of three implementation variants: LSTM, GRU, and IndRNN thereby uncovering deficiencies and suggesting more robust solutions for overflow prediction." @default.
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- W3109469733 date "2020-06-27" @default.
- W3109469733 modified "2023-10-13" @default.
- W3109469733 title "Deep learning for smart sewer systems" @default.
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- W3109469733 doi "https://doi.org/10.1145/3377815.3381379" @default.
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