COMPARISON BETWEEN M5 MODEL TREE AND NEURAL NETWORKS FOR PREDICTION OF SIGNIFICANT WAVE HEIGHT IN LAKE SUPERIOR

By A. Etemad-Shahidi, J. Mahjoobi. 2009

221 citations recorded by the source corpus.

Subfields: Earth-Surface Processes, Environmental Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/b2b9432cd119ba75458f85a8b7b3a12148b2154a

How researchers see this paper

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

In Earth-Surface Processes

1. Fearghal O'Donncha
2. Yushan Zhang
3. Scott C. James

In Environmental Engineering

1. Mohammad Zounemat‐Kermani
2. Behrooz Keshtegar
3. Hamed Ghohani Arab
4. Ozgur Kisi
5. Himan Shahabi
6. Fearghal O'Donncha
7. Binh Thai Pham
8. Yushan Zhang

In Oceanography

1. Fearghal O'Donncha
2. Yushan Zhang
3. Scott C. James

Citations in the corpus

• A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers (2020, background): “The first one indicates a choice between a number of alternatives and another represents a classification or decision issue [65].”
• Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, background): “referred to references (Etemad-Shahidi and Mahjoobi 2009; Pal and Deswal 2009) for comprehensive information about the M5Tree model.”
• A machine learning framework to forecast wave conditions (2017, methodology): “While machine learning has been used to predict wave conditions [11, 12, 13, 14, 15, 16], it has not been used in the context of a surrogate model as defined below.”