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Missing Data Toolbox for Air Quality Datasets

dc.contributor.authorKolehmainen, Mikko
dc.contributor.authorJunninen, Heikki
dc.contributor.authorNiska, Harri
dc.contributor.authorPatama, Toni
dc.contributor.authorRuuskanen, Anna
dc.contributor.authorTuppurainen, Kari
dc.contributor.authorRuuskanen, Juhani
dc.contributor.editorPillmann, Werner
dc.contributor.editorTochtermann, Klaus
dc.date.accessioned2019-09-16T09:33:06Z
dc.date.available2019-09-16T09:33:06Z
dc.date.issued2002
dc.description.abstractThe objective of the study was to find a useful missing data imputing method for air quality forecasting applications. The univariate methods studied were the linear interpolation, spline and nearest neighbour (univariate) interpolation. Multivariate methods studied were multivariate nearest neighbour (NN), Self-Organising Map (SOM) and Multi-Layer Perceptron (MLP). Additionally, a new approach was developed where univariate methods were combined with multivariate methods in order to utilise the best properties of both approaches. The results in general showed that the best overall performance can be achieved by combining univariate and multivariate methods and that the way of combining is dependent on the variable inspected. Based on these results a Missing Data Toolbox (MDT) with a Graphical User Interface (GUI) in Matlab environment was created. The MDT encapsulates the different algorithms and enables the treatment of missing data in a coherent way. The MDT and GUI were tested on Windows and Linux environments.de
dc.description.urihttp://enviroinfo.eu/sites/default/files/pdfs/vol105/0445.pdfde
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/26956
dc.publisherIGU/ISEP
dc.relation.ispartofEnvironmental Communication in the Information Society - Proceedings of the 16th Conference
dc.relation.ispartofseriesEnviroInfo
dc.titleMissing Data Toolbox for Air Quality Datasetsde
dc.typeText/Conference Paper
gi.citation.publisherPlaceWien
gi.conference.date2002
gi.conference.locationWien
gi.conference.sessiontitleEnvironmental Statistics

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