Downscaling of precipitation for climate change scenarios: A support vector machine approach

By Shivam Tripathi, V. V. Srinivas, Ravi S. Nanjundiah. 2006

599 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c50795ab1f32c557db2b64e40b51e48cd8790d79

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In Atmospheric Science

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, unknown): “A low-dimensional nonlinear problem can be transformed into a highdimensional linear problem using a kernel function (Vapnik 2000; Tripathi et al. 2006).”
Using oceanic‐atmospheric oscillations for long lead time streamflow forecasting (2007, methodology): “This is the most widely used approach and has been well documented [Cherkassky and Ma, 2004; Gill et al., 2006; Tokar and Markus, 2000; Asefa et al., 2006; Tripathi et al., 2006].”
Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows (2013, unknown): “Many authors have used the NCEP/NCAR reanalysis data set for the calibration and validation of their downscaling models in the past (Tripathi et al ., 2006; Anandhi et al ., 2008, 2009; Ghosh and Mujumdar, 2008; Tisseuil et al ., 2010).”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “SVM was also shown to be useful in statistical downscaling of atmospheric forcings (H. Chen et al., 2010; Tripathi et al., 2006).”
A comparison of three methods for downscaling daily precipitation in the Punjab region (2011, methodology): “Support vector machine (SVM) uses the structural risk minimization (SRM) principle that minimizes an upper bound on the expected risk (Tripathi et al., 2006).”
Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment (2007, methodology): “…neural network [Wilby et al., 1998; Tripathi and Srinivas, 2005], fuzzy rule-based system [Bardossy et al., 2005], support vector machine [Tripathi et al., 2006], analogue method [Wetterhall et al., 2005, Gutierrez et al., 2004], etc. have been used to derive predictor-preditand…”
Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, background): “In contrast, several studies (e.g., Schoof and Pryor 2001; Tripathi et al. 2006; Tolika et al. 2007) have illustrated that linear transfer functions have failed to estimate adequately the relationships between the predictors and predictands in their SD problems.”
Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling (2019, background): “For instance, SVM performed better than ANN in predicting groundwater levels [25], runoff and sediment yield simulation [26], flood stage prediction [27], rainfall–runoff modeling [28], river flow forecasting [29,30], long-term discharge prediction [31], and modeling discharge-suspended sediment rel”