Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 3-2-1: Artificial Intelligence machine learning 1 Location: HSB0 Session Chair: Eran Friedler Session Chair: Namrata Karki | |
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11:30am - 11:45am
A probabilistic framework for urban wastewater flow forecasting University of Exeter, United Kingdom Sewer flow forecasting is critical for managing the performance of sewer networks and their treatment plants. While simulators have been used in modelling the sewer flow for years, emulators recently have gained attention in making predictions with a higher computational speed and feasibility. In this research, a framework is proposed based on multi-input single-output Gaussian Processes for predicting sewer flow using time and rainfall as inputs. The predictions are presented as Gaussian distributions, showing the confidence levels. The results of the GPR on the data of a sewer system in this study demonstrated a robust performance of the model with 93.6% coverage of the predictions in the 95% credible interval and 89.5 L/s of RMSE. | |