A fuzzy KNN-based model for significant wave height prediction in large lakes

• Autorzy: Nikoo M. R. , Kerachian R. , Alizadeh M. R.

Identyfikatory

DOI

10.1016/j.oceano.2017.09.003

• Języki publikacji: EN

Abstrakty

EN

Some algorithms based on fuzzy set theory (FST) such as fuzzy inference system (FIS) and adaptive-network-based fuzzy inference system (ANFIS) have been successfully applied to significant wave height (SWH) prediction. In this paper, perhaps for the first time, the fuzzy K-nearest neighbor (FKNN) algorithm is utilized to develop a fuzzy wave height prediction model for large lakes, where the fetch length depends on the wind direction. As fetch length (or wind direction) can affect the wave height in lakes, this variable is also considered as one of the inputs of the prediction model. The results of the FKNN model are compared with those of some soft computing techniques such as Bayesian networks (BNs), regression tree induction (named M5P), and support vector regression (SVR). The developed FKNN model is used for SWH prediction in the western part of Lake Superior in North America. The results show that the FKNN and M5P model can outperform the other soft computing techniques.

Słowa kluczowe

EN

significant wave height prediction; fuzzy K-nearest neighbor; Bayesian networks; support vector regression; regression tree induction

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2018
• Tom: No. 60 (2)
• Strony: 153--168
• Opis fizyczny: Bibliogr. 36 poz., mapy, rys., tab., wykr.

Twórcy

autor

Nikoo M. R.

• Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran

autor

Kerachian R.

• School of Civil Engineering and Center of Excellence for Engineering and Management of Civil Infrastructures, College of Engineering, University of Tehran, Tehran, Iran

autor

Alizadeh M. R.

• Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).