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

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.

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

“…However, there is no standard or generally acknowledged list or a particular set of properties that can be utilized to precisely depict and abridge an event. Based on literature reviews, the often-used properties in various studies are duration, depth, mean rain rate, maximum rainfall intensity and intra-event dry periods and hydrology studies have used event peak intensity for an aggregation scale at different time steps[10] [14] [24][32]. We also include the property describing the fraction of intra-event rainless periods, previous inter-event time and the position of the peak in the event (time of peak and fraction of event depth until event maximum), which are relevant overland flow generation, runoff, and infiltration[33] [34][35] [36].…”

Clustering of Rainfall Types Using Micro Rain Radar and Laser Disdrometer Observations in the Tropical Andes

“…This is accomplished by identifying different groups of instances (i.e., rainfall events) with similar features (i.e., the rainfall characteristics such as duration, DSD, etc.) within each group in a data set [59]. The main idea behind the k-means algorithm is grouping n points of m dimensions into k clusters, so that for each cluster, the square of the Euclidean distance between the x points that belongs to n and the centroid of the cluster is minimal (Equation (1)) [58,60,61].…”

Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka

“…The Indian Ocean Dipole (IOD) is an oscillation of sea surface temperature in the equatorial Indian Ocean between the Arabian Sea and south of Indonesia (Bureau of Meteorology, 2017). The IOD is identified as relevant to the climate of Australia (Power et al, 1999) and countries surrounded by the Indian Ocean in southern Asia (Chaudhari et al, 2013;Maity and Nagesh Kumar, 2006;Qiu et al, 2014;Surendran et al, 2015). The dipole mode index (DMI) is used to represent the IOD capturing the west and eastern equatorial sea surface temperature gradient.…”

“…Success in making useful forecasts is often achieved by considering climate teleconnections such as the El Niño-Southern Oscillation (ENSO) as related to sea surface temperature variations and air pressure over the globe using empirical data (Amarasekera et al, 1997;Denise et al, 2017;Korecha and Sorteberg, 2013;Seibert et al, 2017). Also, modes of variability of other tropical oceans can be related to regional precipitation (Dettinger and Diaz, 2000;Eden et al, 2015;Maity and Nagesh Kumar, 2006;Malmgren et al, 2007;Ranatunge et al, 2003;Suppiah, 1996;Roplewski and Halpert, 1996). For example, the effect of the Indian Ocean Dipole (IOD) is identified as independent of the ENSO effect (Eden et al, 2015).…”