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

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 convolutio...

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Autores principales:	Caifen He, Qiaote Chen, Xuyuan Fang, Yangzhang Zhou, Randi Fu, Wei Jin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	convolutional neural network fishing haven anchorage time series wind speed forecasting deep learning Science Q
Acceso en línea:	https://doaj.org/article/e91d6d177bf446c6af7b82771d445deb
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spelling	oai:doaj.org-article:e91d6d177bf446c6af7b82771d445deb2021-12-01T14:06:47ZWind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network2296-646310.3389/feart.2021.731803https://doaj.org/article/e91d6d177bf446c6af7b82771d445deb2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/feart.2021.731803/fullhttps://doaj.org/toc/2296-6463Wind 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.Caifen HeQiaote ChenXuyuan FangYangzhang ZhouRandi FuWei JinFrontiers Media S.A.articleconvolutional neural networkfishing haven anchoragetime serieswind speed forecastingdeep learningScienceQENFrontiers in Earth Science, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network fishing haven anchorage time series wind speed forecasting deep learning Science Q
spellingShingle	convolutional neural network fishing haven anchorage time series wind speed forecasting deep learning Science Q Caifen He Qiaote Chen Xuyuan Fang Yangzhang Zhou Randi Fu Wei Jin Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
description	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.
format	article
author	Caifen He Qiaote Chen Xuyuan Fang Yangzhang Zhou Randi Fu Wei Jin
author_facet	Caifen He Qiaote Chen Xuyuan Fang Yangzhang Zhou Randi Fu Wei Jin
author_sort	Caifen He
title	Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
title_short	Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
title_full	Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
title_fullStr	Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
title_full_unstemmed	Wind Speed Forecasting in Fishing Harbor Anchorage Using a Novel Deep Convolutional Neural Network
title_sort	wind speed forecasting in fishing harbor anchorage using a novel deep convolutional neural network
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e91d6d177bf446c6af7b82771d445deb
work_keys_str_mv	AT caifenhe windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork AT qiaotechen windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork AT xuyuanfang windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork AT yangzhangzhou windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork AT randifu windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork AT weijin windspeedforecastinginfishingharboranchorageusinganoveldeepconvolutionalneuralnetwork
_version_	1718405091810082816

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

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