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Learning from data for wind- wave forecasting

机译：从数据中学习以进行风浪预测

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Along with existing numerical process models describing the wind-wave interaction, the relatively recent development in the area of machine learning make the so-called data-driven models more and more popular. This paper presents a number of data-driven models for wind-wave process at the Caspian Sea. The problem associated with these models is to forecast significant wave heights for several hours ahead using buoy measurements. Models are based on artificial neural network (ANN) and instance-based learning (IBL) To capture the wind-wave relationship at measurement sites, these models use the existing past time data describing the phenomenon in question. Three feed-forward ANN models have been built for time horizon of 1, 3 and 6 h with different inputs. The relevant inputs are selected by analyzing the average mutual information (AMI). The inputs consist of priori knowledge of wind and significant wave height. The other six models are based on IBL method for the same forecast horizons. Weighted k-nearest neighbors (k-NN) and locally weighted regression (LWR) with Gaussian kernel were used. In IBL-based models, forecast is made directly by combining instances from the training data that are close (in the input space) to the new incoming input vector. These methods are applied to two sets of data at the Caspian Sea. Experiments show that the ANNs yield slightly better agreement with the measured data than IBL. ANNs can also predict extreme wave conditions better than the other existing methods.
机译：随着描述风波相互作用的现有数值过程模型的发展，机器学习领域中相对较新的发展使所谓的数据驱动模型越来越受欢迎。本文介绍了里海风浪过程的许多数据驱动模型。与这些模型相关的问题是使用浮标测量来预测未来几个小时的显着波高。这些模型基于人工神经网络（ANN）和基于实例的学习（IBL），以捕获测量地点的风波关系，这些模型使用描述问题现象的现有过去时间数据。针对1、3、6 h的时间范围，使用不同的输入建立了三个前馈ANN模型。通过分析平均互信息（AMI）选择相关输入。输入包括风的先验知识和显着的波高。其他六个模型基于相同的预测范围的IBL方法。使用加权k最近邻（k-NN）和具有高斯核的局部加权回归（LWR）。在基于IBL的模型中，预测是通过将来自训练数据的实例（在输入空间中）与新的传入输入向量相近来直接进行组合来进行的。这些方法适用于里海的两组数据。实验表明，与IBL相比，人工神经网络与实测数据的一致性更好。人工神经网络还可以比其他现有方法更好地预测极端波浪条件。

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