Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network - He, Caifen; Chen, Qiaote; Fang, Xuyuan; Zhou, Yangzhang; Fu, Randi; Jin, Wei (Frontiers in Earth Science, 2021)
Wind speed forecasting is an important issue in Marine fisheries. Improving the accuracy of wind speed forecasting is helpful to reduce the loss of fishery economy caused by strong wind. This paper proposes a wind speed forecasting method for fishing harbor anchorage based on a novel deep convolutional neural network. By combining the actual monitoring data of the automatic weather station with the Numerical Weather Prediction (NWP) products, the proposed method constructing a deep convolutional neural network was based wind speed forecasting model. The model includes a one-dimensional convolution module(1D-CM) and a two-dimensional convolution module(2D-CM), in which 1D-CM extracts the time series features of the meteorological data, and 2D-CM is used to mine the latent semantic information from the outputs of 1D-CM. In order to alleviate the over-fitting problem of the model, the L2 regularization and the Dropout strategies are adopted in the training process, which improves the generalization of the model with higher reliability for wind speed prediction. Simulation experiments were carried out, using the 2016 wind speed and related meteorological data of a sheltered anchorage in Xiangshan, Ningbo, China. The results showed that, for wind speed forecast in the next 1-hour, the proposed method outperform the traditional methods in terms of prediction accuracy, the mean absolute error(MAE) and the mean absolute percentage error(MAPE) of the proposed method are 0.3945m /s and 5.71% respectively. Moreover, the proposed method has some advantages in long-time wind speed prediction, which shows that the method is also meet the needs of meteorological services.