A fuzzy KNN-based model for significant wave height prediction in large lakes
Summary: 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-04-01
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| Series: | Oceanologia |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0078323417300945 |
| _version_ | 1818135543285809152 |
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| author | Mohammad Reza Nikoo Reza Kerachian Mohammad Reza Alizadeh |
| author_facet | Mohammad Reza Nikoo Reza Kerachian Mohammad Reza Alizadeh |
| author_sort | Mohammad Reza Nikoo |
| collection | DOAJ |
| description | Summary: 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. Keywords: Significant wave height prediction, Fuzzy K-nearest neighbor, Bayesian networks, Support vector regression, Regression tree induction |
| first_indexed | 2024-12-11T09:26:11Z |
| format | Article |
| id | doaj.art-654fee81b0a54b80a27598886f4b657f |
| institution | Directory Open Access Journal |
| issn | 0078-3234 |
| language | English |
| last_indexed | 2024-12-11T09:26:11Z |
| publishDate | 2018-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Oceanologia |
| spelling | doaj.art-654fee81b0a54b80a27598886f4b657f2022-12-22T01:13:08ZengElsevierOceanologia0078-32342018-04-01602153168A fuzzy KNN-based model for significant wave height prediction in large lakesMohammad Reza Nikoo0Reza Kerachian1Mohammad Reza Alizadeh2Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran; Corresponding author at: Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Zand Street, Shiraz 7134851156, Iran. Tel.: +98 713 6133497; fax: +98 711 6473161.School of Civil Engineering and Center of Excellence for Engineering and Management of Civil Infrastructures, College of Engineering, University of Tehran, Tehran, IranDepartment of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, IranSummary: 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. Keywords: Significant wave height prediction, Fuzzy K-nearest neighbor, Bayesian networks, Support vector regression, Regression tree inductionhttp://www.sciencedirect.com/science/article/pii/S0078323417300945 |
| spellingShingle | Mohammad Reza Nikoo Reza Kerachian Mohammad Reza Alizadeh A fuzzy KNN-based model for significant wave height prediction in large lakes Oceanologia |
| title | A fuzzy KNN-based model for significant wave height prediction in large lakes |
| title_full | A fuzzy KNN-based model for significant wave height prediction in large lakes |
| title_fullStr | A fuzzy KNN-based model for significant wave height prediction in large lakes |
| title_full_unstemmed | A fuzzy KNN-based model for significant wave height prediction in large lakes |
| title_short | A fuzzy KNN-based model for significant wave height prediction in large lakes |
| title_sort | fuzzy knn based model for significant wave height prediction in large lakes |
| url | http://www.sciencedirect.com/science/article/pii/S0078323417300945 |
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