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Machine Learning in weakly nonlinear systems: A Case study on Significant wave heights
This paper proposes a machine learning method based on the Extra Trees (ET) algorithm for forecasting Significant Wave Heights in oceanic waters.
A LightGBM based Forecasting of Dominant Wave Periods in Oceanic Waters
For 1 day ahead prediction, the proposed algorithm has SI, Bias, CC, and RMSE of 0. 09, 0. 00, 0. 97, and 1. 78 compared to 0. 268, 0. 40, 0. 63, and 2. 18 for the European Centre for Medium-range Weather Forecasts (ECMWF) model, which outperforms all the other methods in the test dataset.
Random Forest Classifier Based Prediction of Rogue waves on Deep Oceans
In this paper, we present a novel approach for the prediction of rogue waves in oceans using statistical machine learning methods.
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