Abstract
One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H s . This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.
Highlights
Ocean waves are oscillations of the free sea surface caused by the wind
This method is based on the use of multilayer perceptrons (MLPs) for implementing a non-linear function that relates the selected input parameters with Hs
The parameters selected in our case √study are: the square root of the signal-to-noise ratio ( SNR), the peak wave length, and the mean wave period (Tm)
Summary
Ocean waves are oscillations of the free sea surface caused by the wind. Under severe meteorological conditions, ocean waves can be dangerous for human marine activities, such as navigation, on- and off-shore management, etc. The sea state parameters commonly estimated from the wave spectrum is Hs. Since non-coherent marine radars are not radiometrically calibrated, Hs cannot be directly obtained from the un-scaled (often logarithmically amplified as a function of range) backscatter image values. MLP-based Hs estimator: architecture, data processing, and computational cost This section presents the proposed ANN-based Hs estimator, discussing what kind and how many sea state parameters are considered, and what ANN architecture (type, activation functions and size) is selected. Each estimator takes the corresponding sea state parameters given by the WaMoS II software This figure summarizes the architecture (type, activation functions, and size) of the ANN selected in our case of study, as well as the way the data is processed. This error is computed in the kthiteration of the algorithm for a set of MTrain Hs measurements as: eMS[k]
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