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Water 2016, 8(3), 87; doi:10.3390/w8030087

Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques

1
Department of Environmental Engineering, Technical University of Denmark, Miljøvej, B115, Lyngby DK-2800 Kgs., Denmark
2
Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Matematiktorvet, B303, Lyngby DK-2800 Kgs., Denmark
3
Smith School of Enterprise & the Environment, Oxford University, South Parks Road, Oxford OX1 3QY, UK
4
ICT Center of Excellence, Carnegie Mellon University, Kigali, Rwanda
*
Author to whom correspondence should be addressed.
Academic Editors: Paolo Reggiani and Ezio Todini
Received: 18 December 2015 / Revised: 19 February 2016 / Accepted: 29 February 2016 / Published: 4 March 2016
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
View Full-Text   |   Download PDF [1820 KB, uploaded 4 March 2016]   |  

Abstract

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. View Full-Text
Keywords: rainfall classification; water system control; clustering; quadratic discriminant analysis rainfall classification; water system control; clustering; quadratic discriminant analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Löwe, R.; Madsen, H.; McSharry, P. Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques. Water 2016, 8, 87.

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