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Artificial neural network to translate offshore satellite wave data to coastal locations

机译：人工神经网络将近海卫星波数据转换为沿海位置

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摘要

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast 6f India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-a-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.
机译：由于涉及卫星感测的空间平均，因此所收集的观测数据的使用通常仅限于近海区域。本文讨论了一种技术，该技术可从指定的沿海地点的卫星在较深的近海位置收集的波高值来获得重要的波高。该技术基于径向基函数（RBF）和前馈反向传播（FFBP）类型的人工神经网络（ANN）方法。卫星感测到的重要波高数据；将平均波浪周期和风速作为网络的输入，以便在位于印度西海岸6f的沿海地点获得明显的波浪高度。网络输出与目标观测值的定性和定量比较表明，与简单的技术（如统计回归）相比，所选网络在此类应用中很有用。基本的FFBP网络可以更正确地预测较高的波浪，尽管从整体准确性的角度来看，这样的网络吸引力较小。与卫星观测不同，浮标数据的收集是昂贵的，因此，通常将其用于较少的位置并使用较短的时间。如本研究所示，该网络可以用浮标数据样本进行训练，并且可以基于更持久的卫星观测流量，进一步用于沿海位置的例行波浪预报。

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