Soil moisture prediction with feature selection using a neural network

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)

Abstract

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, 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.

Original languageEnglish
Title of host publicationProceedings - Digital Image Computing: Techniques and Applications, DICTA 2008
Pages130-136
Number of pages7
DOIs
Publication statusPublished - 2008
EventDigital Image Computing: Techniques and Applications, DICTA 2008 - Canberra, ACT, Australia
Duration: 1 Dec 20083 Dec 2008

Other

OtherDigital Image Computing: Techniques and Applications, DICTA 2008
CountryAustralia
CityCanberra, ACT
Period1/12/083/12/08

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