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Soil moisture prediction with feature selection using a neural network

Song, Junlei; Wang, Dianhong; Liu, Nianjun; Cheng , Li; Du, Lan; Zhang, Ke

Description

For the problem of soil moisture prediction, existing approaches in literature [6, 11] usually utilize as many decision factors as possible, e.g. rainfall, solar irradiance, drainage, etc. However, the redundancy aspect of the decision factors has not been studied rigorously. Previous research work in data mining has shown that removing redundant features improves rather than deteriorates the prediction accuracy. In this paper, we propose an approach to the problem of soil moisture prediction,...[Show more]

dc.contributor.authorSong, Junlei
dc.contributor.authorWang, Dianhong
dc.contributor.authorLiu, Nianjun
dc.contributor.authorCheng , Li
dc.contributor.authorDu, Lan
dc.contributor.authorZhang, Ke
dc.contributor.editorA. Robles-Kelly
dc.coverage.spatialCanberra Australia
dc.date.accessioned2015-12-10T22:32:20Z
dc.date.createdDecember 1-3 2008
dc.identifier.isbn9780769534565
dc.identifier.urihttp://hdl.handle.net/1885/55715
dc.description.abstractFor the problem of soil moisture prediction, existing approaches in literature [6, 11] usually utilize as many decision factors as possible, e.g. rainfall, solar irradiance, drainage, etc. However, the redundancy aspect of the decision factors has not been studied rigorously. Previous research work in data mining has shown that removing redundant features improves rather than deteriorates the prediction accuracy. In this paper, we propose an approach to the problem of soil moisture prediction, which integrates two components: feature selection and prediction model: A method is proposed for feature selection that effectively removes the redundant decision factors; This is followed by a feedforward neural network to make prediction based on the retained (i.e. non-redundant) decision factors. Empirical simulations demonstrate the effectiveness of the proposed approach. In particular, with the help of the proposed feature selection component to remove redundant decision factors, the proposed approach is shown to give better prediction accuracy with lower data collection cost.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesDigital Image Computing: Techniques and Applications (DICTA 2008)
dc.sourceProceedings of Digital Image Computing: Techniques and Applications (DICTA 2008)
dc.subjectKeywords: Data collection; Decision factors; Empirical simulations; Feature selection; Prediction accuracy; Prediction model; Redundant features; Solar irradiances; Two-component; Feedforward neural networks; Groundwater; Information management; Mining; Moisture de
dc.titleSoil moisture prediction with feature selection using a neural network
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2008
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB337
local.type.statusPublished Version
local.contributor.affiliationSong, Junlei, China University of Geosciences
local.contributor.affiliationWang, Dianhong, China University of Geosciences
local.contributor.affiliationLiu, Nianjun, College of Engineering and Computer Science, ANU
local.contributor.affiliationCheng , Li, College of Engineering and Computer Science, ANU
local.contributor.affiliationDu, Lan, College of Engineering and Computer Science, ANU
local.contributor.affiliationZhang, Ke, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage130
local.bibliographicCitation.lastpage136
local.identifier.doi10.1109/DICTA.2008.35
dc.date.updated2016-02-24T11:44:14Z
local.identifier.scopusID2-s2.0-67549138600
CollectionsANU Research Publications

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