Artificial neural networks ensemble methodology to predict significant wave height

• URL: http://arxiv.org/abs/2509.14020v1
• Date: Wed, 17 Sep 2025 14:25:57 GMT
• Title: Artificial neural networks ensemble methodology to predict significant wave height
• Authors: Felipe Crivellaro Minuzzi, Leandro Farina,
• Abstract summary: We present a methodology to create an ensemble of different artificial neural networks, namely, RNN, LSTM, CNN and a hybrid CNN-LSTM.<n>The networks are trained using NOAA's numerical reforecast data and target the residual between observational data and the numerical model output.<n>Results show that our framework is capable of producing high efficient forecast, with an average accuracy of $80%$, that can achieve up to $88%$ in the best case scenario.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the computational power increase, machine learning algorithms have been applied as surrogate to traditional numerical models, yielding comparative or better results. In this work, we present a methodology to create an ensemble of different artificial neural networks architectures, namely, MLP, RNN, LSTM, CNN and a hybrid CNN-LSTM, which aims to predict significant wave height on six different locations in the Brazilian coast. The networks are trained using NOAA's numerical reforecast data and target the residual between observational data and the numerical model output. A new strategy to create the training and target datasets is demonstrated. Results show that our framework is capable of producing high efficient forecast, with an average accuracy of $80\%$, that can achieve up to $88\%$ in the best case scenario, which means $5\%$ reduction in error metrics if compared to NOAA's numerical model, and a increasingly reduction of computational cost.

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