Show simple item record

dc.contributor.authorSallis, Pen_NZ
dc.contributor.authorHernandez, Sen_NZ
dc.contributor.authorShanmuganathan, Sen_NZ
dc.date.accessioned2019-01-13T22:29:32Z
dc.date.available2019-01-13T22:29:32Z
dc.identifier.citationIn proceedings of the 3rd International Conference on Machine Learning and Computing (ICMLC 2011), Singapore 26-28, February 2011, Vol. 1, pp. 623-628.
dc.identifier.urihttp://hdl.handle.net/10292/12147
dc.description.abstractThis paper describes the selection of a state-space estimation method for application to the emerging research domain of agrometeorology. The work comes from a wider geocomputational research programme that relates to climate and environment monitoring and subsequent data analysis. In particular, the data currently being collected refers to meso-micro climates in vineyards across eight countries. It is terrestrial in kind, being in the context of near-ground truth continuous data. The time-related nature of the data, being continuous across a geospatial plane, gives rise to the need for mathematical models that are intrinsically spatio-temporal and while effective in their robust adequacy, are also computationally efficient. State-space models are considered a class of model within the time-series literature but they have some uniquely distinguishing features for continuous multivariate data representation. Ensemble Kalman Filter models are Bayesian based estimators of multiple realisations of statespaces over time, so are proposed here as applicable to this analytical process domain.en_NZ
dc.publisherIEEE Operations Center
dc.relation.urihttps://www.tib.eu/en/search/id/TIBKAT%3A817981446/Proceedings-ICMLC-2011-2011-3rd-International-Conference/
dc.rightsCopyright © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectGeocomputation; Estimation; Agronomy; Meteorology; Sensors; Monitoring telemetry
dc.titleDynamic Multivariate Continuous Data State-space Estimation for Agrometeorological Event Anticipationen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
pubs.elements-id6557


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record