• Corpus ID: 53438302

DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning

@article{Zhang2018DeepCSOFO,
  title={DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning},
  author={Duo Zhang and Geir Lindholm and Harsha Ratnaweera},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.06368}
}
Combined Sewer Overflow (CSO) is a major problem to be addressed by many cities. [] Key Method The proposed model provided an intermediate methodology that combines the flexibility of data-driven methods and the rich information contained in deterministic methods while avoiding the drawbacks of these two methods. A comparison of the results demonstrated that the deep learning based multi-task model is superior to the traditional methods.
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