• Jun 14, 2017 News!Vol.6, No.3 has been indexed by EI(Inspec)!   [Click]
  • May 03, 2016 News!Vol.5, No.5 has been indexed by EI(Inspec)!   [Click]
  • May 03, 2016 News!Vol.5, No.4 has been indexed by EI(Inspec)!   [Click]
General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 672-676 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.212

Dynamic Multivariate Continuous Data State-Space Estimation for Agrometeorological Event Anticipation

Philip Sallis, Sergio Hernández, and Subana Shanmuganathan
Abstract—This 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 geo-spatial 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 state-spaces over time, so are proposed here as applicable to this analytical process domain.

Index Terms—Geocomputation; estimation; agronomy; meteorology; sensors; monitoring telemetry.

P Sallis is with Geoinformatics Research Centre (GRC), School of Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland, New Zealand (e-mail: philip.sallis@aut.ac.nz).
S. Hernández is with Laboratorio de Procesamiento, de Información Geoespacial,Universidad Católica del Maule, Talca, Chile. (email:shernandez@ucm.cl ).
S Shanmuganthan is with Geoinformatics Research Centre, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand. (e-mail: subana.shanmuganthan@aut.ac.nz).

[PDF]

Cite:Philip Sallis, Sergio Hernández, and Subana Shanmuganathan, "Dynamic Multivariate Continuous Data State-Space Estimation for Agrometeorological Event Anticipation," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 672-676, 2012.

Copyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net