Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment

@article{Shrestha2015SupportVM,
  title={Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment},
  author={N. Shrestha and S. Shukla},
  journal={Agricultural and Forest Meteorology},
  year={2015},
  volume={200},
  pages={172-184}
}
Abstract Existing models and methods report crop coefficient (Kc) as a function of time but do not consider the variations due to surface conditions, wetting methods, meteorological conditions, and other biophysical factors. These limitations result in erroneous crop evapotranspiration (ETc) estimates, especially for non-standard conditions (e.g. plastic mulch). We present Support Vector Machine (SVM), a data-driven model based on statistical learning theory, for predicting generic Kc and ETc… Expand
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References

SHOWING 1-10 OF 90 REFERENCES
Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine
  • 131
  • PDF
...
1
2
3
4
5
...