Central Germany Precipitation
نویسندگان
چکیده
Many existing methods for temporal trend detection in environmental space-time data are based on grouping observations simply by geographical proximity. Such grouping is often static and thus does not account for changes in space-time data distribution. In this project we evaluate properties of the new data mining algorithm for dynamic detection of space-time patterns, TRUST, proposed by Ciampi et al. [3], [4]. In particular, we explore sensitivity of TRUST in respect to choice of tuning parameters and consider a case study of dynamic cluster detection in yearly precipitation records among 167 stations in Central Germany over 1951–2010.
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