Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network

Caifen He,Yangzhang Zhou,Qiaote Chen,Randi Fu,Wei Jin,Xuyuan Fang

doi:10.3389/feart.2021.731803

Caifen He, Yangzhang Zhou + Show 4 more

Open Access

https://doi.org/10.3389/feart.2021.731803

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Abstract

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 overfitting 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 h, 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 0.3945 m/s and 5.71%, respectively.

Highlights

In the coastal areas of China, marine resources are abundant, and the local economic development mainly depends on fishing, marine transportation, marine oil, gas industry, etc
In order to comprehensively evaluate the predictive performance of the model, mean absolute error (MAE), mean absolute percentage error (MAPE), and root of the mean squared error (RMSE) were used as error evaluation indexes; the expressions of error indexes are as follows: MAE
Deep convolutional neural network is applied to the prediction of wind speed in harbor anchorage, and a deep convolutional neural network model based on 1D-CNN and 2D-CNN is proposed

Summary

INTRODUCTION

In the coastal areas of China, marine resources are abundant, and the local economic development mainly depends on fishing, marine transportation, marine oil, gas industry, etc. The deep convolutional neural network model proposed in this paper takes the time series feature maps as the input of the model, which is similar to the word vector representation method in natural language processing. The above content has given the basic structure of the proposed model and the construction method of the model input, and the details of the model are elaborated: One dimensional convolutional neural network: First, the model proposed adopts the 1D-CNN layer to process the input time series data. In order to train the proposed model and deal with the task of predicting wind speed in the hour, a set of training feature maps was constructed from the time series of the original meteorological parameters, feature maps {x1, x2, /xm}, where xi ∈ R8×8. The expression of the objective loss function minimized after L2 norm is adopted is:

F λ2 2

EXPERIMENTAL RESULTS

CONCLUSION

DATA AVAILABILITY STATEMENT