Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine 1
By Xuesong Zhang, Raghavan Srinivasan, M. V. Van Liew. 2009
132 citations recorded by the source corpus.
Subfields: Environmental Engineering, Water Science and Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Saman Razavi
- Donald H. Burn
- Bryan Tolson
- Xuesong Zhang
- Raghavan Srinivasan
- D. Bosch
- Wei Gong
- Qingyun Duan
In Water Science and Technology
- Saman Razavi
- Donald H. Burn
- Bryan Tolson
- Xuesong Zhang
- Raghavan Srinivasan
- D. Bosch
- Wei Gong
- Qingyun Duan
Citations in the corpus
- An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “, 1984), Random Forest (RF) method (Breiman, 2001), MARS (Friedman, 1991), Support Vector Machines (SVMs) (Zhang et al., 2009), Artificial Neural Networks (ANNs) (Behzadian et al.”
- Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “Zhang et al. (2009c) compared five global optimization algorithms for parameter calibration of SWAT in four basins, and their results show the advantage of GA over other algorithms for calibrating SWAT.”
- Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “…is what we refer to as the basic sequential approach (also called off-line in Jin, 2005); it has been used in different environmental and water resources problems (e.g., in Broad et al., 2005; Johnson and Rogers, 2000; Khu and Werner, 2003; Liong et al., 2001; Zhang et al., 2009; Zou et al., 2007).”
- Review of surrogate modeling in water resources (2012, background): “, 2002; Regis and Shoemaker, 2007a], support vector machines (SVMs) [Zhang et al., 2009], multivariate adaptive regression splines (MARS) [Friedman, 1991; Jin et al.”