118 research outputs found
Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques
This study evaluated methods for automated classification of rain events into groups of “high” and “low” spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities. A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of “high” variability, while the k-means provided the smallest number of rain events falsely identified as being of “high” variability (false hits). A simple classification method based on a threshold for the observed rainfall intensity yielded a large number of false hits and was thus outperformed by the other two methods
Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall
Automatiseret simplificering af 1D hydraulisk model – med hensyn til 1D-2D oversvømmelsesberegninger
State-space adjustment of radar rainfall and stochastic flow forecasting for use in real-time control of urban drainage systems
Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems
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