Predicting lake wave height based on regression classification and multi input–single output soft computing models

This study presents multi input–single output (MISO) soft computing models for forecasting the significant wave height (Hs) of lakes and investigates the impact of input variables for designing the prediction models. Wind direction and speed, air and water temperature changes, wave period and direction, and sea level pressure variables are used as input parameters for the model design to predict the Hs of Michigan Lake. The M5 regression tree (M5T) and robust regression (RR) are used to study the effectiveness of input parameters that impact Hs prediction model. Furthermore, four MISO soft computing models, namely regression (RR and M5T), artificial neural network (ANN), and least square support vector machine (LSSVM), are evaluated and compared to estimate the best model for predicting the Hs. The results show that the wave’s period variable has high impact for prediction of lake Hs values, while the other variables have the same impacts on the prediction accuracy of Hs. The comparison between the developed models shows that the four model performances are acceptable in the training stage, while the MISO-ANN model gives better precision for the prediction of Hs of the lake in the testing stage.