1 |
Ahmad, S., Kalra, A. and Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33, 69-80.
DOI
ScienceOn
|
2 |
Karatzoglou, A., Meyer, D. and Hornik, K. (2006). Support vector machine in R. Journal of Statistical Software, 15, 1-28.
|
3 |
Kim, G. and Barros, A. P. (2002). Space-time characterization of soil moisture from passive microwave remotely sensed imagery and ancillary data. Remote Sensing of Environment, 81, 393-403.
DOI
ScienceOn
|
4 |
Laio, F. (2006). A vertically extended stochastic model of soil moisture in the root zone. Water Resources Research, 42, W02406, doi:10.1029/2005WR004502.
DOI
ScienceOn
|
5 |
Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 209, 415-446.
DOI
ScienceOn
|
6 |
Pasolli, L., Ntarnicola, C. and Bruzzone, L. (2011). Estimating soil moisture with the support vector regression technique. IEEE Geosicence and Remote Sensing Letters, 8, 1080-1084.
DOI
ScienceOn
|
7 |
Rodriguez-Iturbe, I., Vogel, G. K., Rigon, R., Entekhabi, D., Castelli, F. and Rinaldo, A. (1995). On the spatial organization of soil moisture fields. Geophysical Research Letters, 22, 2757-2760.
DOI
ScienceOn
|
8 |
Ruping, S. (2001). SVM kernels for time series analysis. LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitt, 43-50.
|
9 |
Sheikha, V., Saskia Visserb, S. and Stroosnijderb, L. (2009). A simple model to predict soil moisture: Bridging event and continuous hydrological (BEACH) modelling. Environmental Modeling & Software, 24, 542-556.
DOI
ScienceOn
|
10 |
Smoal, A. J. and Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199-222.
DOI
|
11 |
Van Gestel, T., Suykens, J., Baestaens, D., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B. and Vandewalle, J. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12, 809-821.
DOI
ScienceOn
|
12 |
Yang, H., Huang, K., King, I. Lyn, M. R. (2009). Localized support vector regression for time series prediction. Neurocomputing, 72, 2659-2669.
DOI
ScienceOn
|
13 |
Vapnik, V., Golowich, S. and Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems, 9, edited by M. Mozer and M. Jordan and T. Petsche, MIT Press, Cambridge, MA, 281-287.
|