Journal
WATER
Volume 8, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/w8030087
Keywords
quadratic discriminant analysis; water system control; rainfall classification; clustering
Categories
Funding
- Danish Council for Strategic Research, Programme Commission on Sustainable Energy and Environment through the Storm- and Wastewater Informatics (SWI) [34]
- NERC [NE/J017361/1] Funding Source: UKRI
- Natural Environment Research Council [NE/J017361/1] Funding Source: researchfish
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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.
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