Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

By Roohollah Noori, A. Karbassi, A. Moghaddamnia, Dawei Han, M. H. Zokaei-Ashtiani, A. Farokhnia, M. G. Gousheh. 2011

359 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c96f3c521ada98cc96a2510d1fb8763075306ee3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Jan Adamowski
2. Anurag Malik
3. Rahim Barzegar
4. Stefano Galelli
5. A. Castelletti
6. Mohammad Taghi Aalami
7. Ozgur Kisi
8. Kwok-Wing Chau

In Environmental Engineering

1. Jan Adamowski
2. Anurag Malik
3. Ozgur Kisi
4. Quoc Bao Pham
5. Zhongmin Liang
6. Yanlai Zhou
7. Rahim Barzegar
8. D. Souag-Gamane

In Water Science and Technology

1. Jan Adamowski
2. Anurag Malik
3. Yanlai Zhou
4. Rahim Barzegar
5. D. Souag-Gamane
6. Y. Tikhamarine
7. Stefano Galelli
8. Hua Chen

Citations in the corpus

• Tree‐based iterative input variable selection for hydrological modeling (2013, methodology): “…usually high number of candidate input variables in these problems, model-free methods are generally preferred over model-based approaches [Maier et al., 2010], which are mostly used to determine the optimal input set to specific classes of model [e.g., Noori et al., 2011, and references therein].”
• A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “Shabri and Suhartono (2012) and Noori et al. (2011) proposed forecasting models to predict monthly streamflow based on LSSVM and PCA-SVM methods, respectively.”
• Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir (2014, methodology): “2011), much research has been devoted to the formulation and development of SVM for improving the quality of hydrologic prediction (Bray and Han 2004; Yu et al. 2006; Lin et al. 2009; Noori et al. 2011).”
• Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “Lower value of Vratio shows a high degree of predictability for the target y. Gamma, SE, and Vratio having smaller values indicate the mathematical model (e.g. SVR-ALO, SVR-MVO, SVR-SHO, SVR-HHO, SVR-PSO, and SVRBO) of decent quality.”
• Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “Many papers and books (Vapnik 1995; Cortes and Vapnik 1995; Li et al. 2013; Noori et al. 2009, 2011, 2015a; Barzegar et al. 2017; Yu et al. 2017; Babaei et al. 2019) describe the principles and mathematical equations entailed in SVR; therefore, this paper does not include this.”