Deep learning for smart sewer systems
| Autor: | Hemanth Gudaparthi, Reese Johnson, Nan Niu, Harshitha Challa |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Non-functional requirement
business.industry Computer science Deep learning 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Missing data Recurrent neural network Robustness (computer science) 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence Metamorphic testing business computer |
| Zdroj: | ICSE-SEIS |
| DOI: | 10.1145/3377815.3381379 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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