Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling

By John M. Quilty, Jan Adamowski, B. Khalil, M. Rathinasamy. 2016

85 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bd05787247e42bcf720e250088cfd203ca3a74eb

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. Ravinesh C. Deo
2. Zaher Mundher Yaseen
3. Mukesh Tiwari
4. John M. Quilty
5. Mohammad Ali Ghorbani
6. Vahid Karimi
7. O. Terzi
8. Jan Adamowski

In Ocean Engineering

1. Ravinesh C. Deo
2. John M. Quilty
3. Mukesh Tiwari
4. Jan Adamowski

In Water Science and Technology

1. Ravinesh C. Deo
2. Mukesh Tiwari
3. John M. Quilty
4. Zaher Mundher Yaseen
5. Jan Adamowski
6. Mohammad Ali Ghorbani

Citations in the corpus

• Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, methodology): “, weights and biases), whilst acting as a primary tool necessary to improve the performance of the final model (Sedki and Ouazar 2010; Long and Meesad 2013; Quilty et al. 2016).”
• Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “, the EDI) could potentially be identified by nonlinear input variable selection (May et al. 2008; Quilty et al. 2016).”