NDVI Short-Term Forecasting Using Recurrent Neural Networks
DOI:
https://doi.org/10.17770/etr2015vol3.167Keywords:
Artificial Neural Networks, Elman Recurrent Neural Networks, Normalized Difference Vegetation IndexAbstract
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.Downloads
References
M. M. Badamasi, S. A. Yelwa, M. A. AbdulRahim and S. S. Noma, "NDVI threshold classification and change detection of vegetation cover at the Falgore Game Reserve in Kano State, Nigeria," Sokoto Journal of the Social Sciences, vol. 2, no. 2, pp. 174-194.
N. B. Duy and T. T. H. Giang, "Study on vegetation indices selection and changing detection thresholds selection in Land cover change detection assessment using change vector analysis," presented at International Environmental Modelling and Software Society (iEMSs), Sixth Biennial Meeting, Leipzig, Germany, 2012.
E. Sahebjalal and K. Dashtekian, "Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods," African Journal of Agricultural Research, vol. 8, no. 37, pp. 4614-4622, September 26, 2013.
A. Shabri and R. Samsudin, ''Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model,'' Mathematical Problems in Engineering, vol. 2014, article ID 201402, July 2014.
G. Zhang, B. E. Patuwo and M. Y. Hu, ''Forecasting with artificial neural networks: the state of the art,'' International Journal of Forecasting, vol. 14, no. 1, pp. 35-62, March 1998
M. Khashei and M. Bijari, ''An artificial neural network (p, d,q) model for timeseries forecasting,'' Expert Systems with Applications, vol. 37, no. 1, pp. 479-489, January 2010.
C. H. Aladag, E. Egrioglu and C. Kadilar, ''Forecasting nonlinear time series with a hybrid methodology,'' Applied Mathematics Letters, vol. 22, no. 9, pp. 1467-1470, September 2009.
S. M. Jadhav, S. L. Nalbalwar and A. A. Ghatol, ''Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data, ''International Journal of Computer, vol. 44, no. 15, April 2012.
A. Ghosh and M. Chakraborty, ''Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron, '' International Journal of Computer Applications, vol. 60, nr. 13, December 2012.
M. Manobavan, N. S. Lucas, D. S. Boyd and N. Petford, ''Forecasting the interannual trends in terrestrial vegetation dynamics using time series modelling techniques, ''Presented at the ForestSAT Symposium Heriot Watt University, Edinburgh, United Kingdom, 5th - 9th August 2002.
