NDVI Short-Term Forecasting Using Recurrent Neural Networks
Proceedings of the 10th International Scientific and Practical Conference "Environment. Technology. Resources" 2015
Artūrs Stepčenko, Jurijs Čižovs

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.


Atslēgas vārdi
Artificial Neural Networks, Elman Recurrent Neural Networks, Normalized Difference Vegetation Index
DOI
10.17770/etr2015vol3.167
Hipersaite
http://journals.ru.lv/index.php/ETR/article/view/167

Stepčenko, A., Čižovs, J. NDVI Short-Term Forecasting Using Recurrent Neural Networks. No: Proceedings of the 10th International Scientific and Practical Conference "Environment. Technology. Resources", Latvija, Rēzekne, 18.-20. jūnijs, 2015. Rēzekne: Rezekne High School, 2015, 180.-185.lpp. ISBN 978-9984-44-173-3. ISSN 1691-5402. e-ISSN 2256-070X. Pieejams: doi:10.17770/etr2015vol3.167

Publikācijas valoda
English (en)
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