Statistical downscaling of daily precipitation using support vector machines and multivariate analysis
By Shien-Tsung Chen, Pao-Shan Yu, Y. Tang. 2010
242 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/1b23e8c623713a3afb5f34b1729e4e135c2087d3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
Citations in the corpus
- Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, unknown): “This process was widely practised in many of the downscaling studies in the past (Tripathi et al ., 2006; Anandhi et al ., 2008, 2009; Ghosh and Mujumdar, 2008; Chen et al ., 2010).”
- Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, methodology): “Previous researches used the SVC and LDA [24] and only LDA [41] for rainfall-state classification and the SVR for rainfall-amount prediction for the rainfall state “wet day”.”