doi: 10.7763/IJMLC.2012.V2.212
Dynamic Multivariate Continuous Data State-Space Estimation for Agrometeorological Event Anticipation
Philip Sallis1
,
Sergio Hernández2 ,
Subana Shanmuganathan3
- 1Geoinformatics Research Centre (GRC), School of Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland, New Zealand.
- 2Laboratorio de Procesamiento, de Información Geoespacial,Universidad Católica del Maule, Talca, Chile.
- 3Geoinformatics Research Centre, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
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.
Keywords
- Geocomputation;
- estimation;
- agronomy;
- meteorology;
- sensors;
- monitoring
- telemetry
How to Cite
Philip Sallis, Sergio Hernández, and Subana Shanmuganathan, "Dynamic Multivariate Continuous Data State-Space Estimation for Agrometeorological Event Anticipation," International Journal of Machine Learning, vol. 2, no. 5, pp. 672-676, 2012. https://doi.org/10.7763/IJMLC.2012.V2.212
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).