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

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.