Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm

By M. Hejazi, Ximing Cai. 2009

96 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b791878d8ca1ea6828512bc001216f1300d64c03

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

1. John M. Quilty
2. Jan Adamowski
3. B. Khalil
4. M. Rathinasamy
5. Soroosh Sorooshian
6. Ravinesh C. Deo
7. Hoshin V. Gupta
8. Jasper A. Vrugt

In Ocean Engineering

1. John M. Quilty
2. M. Rathinasamy
3. Jan Adamowski
4. B. Khalil
5. Soroosh Sorooshian
6. Ravinesh C. Deo

In Water Science and Technology

1. John M. Quilty
2. M. Rathinasamy
3. Jan Adamowski
4. B. Khalil
5. Soroosh Sorooshian
6. Hoshin V. Gupta
7. Jasper A. Vrugt
8. Thorsten Wagener

Citations in the corpus

• Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “Linear and Nonlinear Autoregressive Data Sets We utilized the AR1, AR9, TAR1, and TAR2 data sets [Sharma, 2000a; Hejazi and Cai, 2009; Sharma and Mehrotra, 2014; Galelli et al., 2014; Chen et al., 2014a] to examine how each input variable selection method performs in terms of high noise and…”
• Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm (2018, background): “Moreover, AI models need low professional requirements from operators and have fast response speeds (Hejazi and Cai, 2009).”