基于Prophet算法的海南近海波浪长时段时序分析与预测

黄心裕; 唐军; 王晓宇

doi:10.12284/hyxb2022086

基于Prophet算法的海南近海波浪长时段时序分析与预测

doi: 10.12284/hyxb2022086

黄心裕^1,,
唐军^1, ,,
王晓宇¹

大连理工大学海岸和近海工程国家重点实验室，辽宁大连 116023

基金项目: 国家重点研发计划（2022YFE0104500）；国家自然科学基金（51579036）。

详细信息

作者简介:
黄心裕（1996-），男，海南省海口市人，从事海岸和近海环境水动力研究。E-mail: 704578677@qq.com

通讯作者:
唐军（1976-），男，宁夏回族自治区中宁县人，从事近岸环境水动力研究。E-mail：jtang@dlut.edu.cn

中图分类号: P731.22
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- 被引次数: 0
出版历程
- 收稿日期: 2021-07-21
- 修回日期: 2021-10-30
- 刊出日期: 2022-04-14

Long term time series analysis and prediction of waves at Hainan offshore zone based on Prophet algorithm

Huang Xinyu^1
,,
Tang Jun^{1
, ,},
Wang Xiaoyu¹

State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116023, China

摘要

摘要: 近年来，以大数据为基础的人工智能算法逐步兴起并被用于海洋波浪短期预测。本文采用2015−2019年海南近海逐时波浪实测时序数据，基于Prophet算法建立了海南近海波浪长时段时序预测模型，分析了2015−2019年海南近海波浪日、月、年变化特性，并对海南近海2020年波浪变化过程进行了预测。结果显示，Prophet算法模型对波浪波高和周期的预测值和实测值整体吻合良好，可有效用于长时段波浪的特性分析和时序预测。
- 海岸和近海 /
- 波浪 /
- Prophet算法 /
- 大数据 /
- 人工智能
Abstract: In recent years, various artificial intelligence algorithms based on big data have gradually emerged and have been applied in short-term time series wave forecasting. Based on the measured time series data of hourly waves in Hainan offshore from 2015 to 2019, a prediction model for long-term time series waves of Hainan offshore based on Prophet algorithm is established in this paper. The daily, monthly and annual variation characteristics of waves in Hainan offshore from 2015 to 2019 are analyzed, and the waves in Hainan offshore in 2020 are predicted. The results show that the predicted values of wave height and period by prophet algorithm model are in good agreement with the measured values. Prophet algorithm model can be effectively used for long-term wave characteristic analysis and time series prediction.
- coast and offshore /
- water wave /
- Prophet algorithm /
- big data /
- artificial intelligence

HTML全文

图 1 Prophet模型构建与预测流程

Fig. 1 Prophet model construction and prediction process

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图 2 测点位置及地形

Fig. 2 Survey points location and topography

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图 3 有效波高训练结果

a. 未考虑极端天气影响；b. 考虑极端天气影响

Fig. 3 Training-fitting results of effective wave heights

a. Without considering extreme weather influence; b. considering extreme weather influence

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图 4 波浪平均周期训练结果

a. 未考虑极端天气影响；b. 考虑极端天气影响

Fig. 4 Training-fitting results of wave mean periods

a. Without considering extreme weather influence; b. considering extreme weather influence

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图 5 2015−2019年波浪日历时变化

Fig. 5 Daily wave duration changes from 2015 to 2019

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图 6 2015−2019年波浪月历时变化

Fig. 6 Monthly wave duration changes from 2015 to 2019

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图 7 2015−2019年波浪年历时变化

Fig. 7 Yearly wave duration changes from 2015 to 2019

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图 8 2020年测点预测和实测波浪有效波高对比

Fig. 8 Comparison between predicted and measured significant wave heights in 2020

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图 9 2020年预测和实测波浪有效波高散点图

Fig. 9 Scatter plot of predicted and measured effective wave heights in 2020

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图 10 2020年测点预测和实测波浪平均周期对比

Fig. 10 Comparison between predicted and measured wave mean periods in 2020

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图 11 2020年预测和实测波浪平均周期散点图

Fig. 11 Scatter plot of predicted and measured wave mean periods in 2020

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表 1 测点坐标及水深

Tab. 1 Location and water depth of survey points

测点纬度经度水深/m

buoy1 19.05°N 110.35°E 15.5
buoy2 19.05°N 110.70°E 101.4
buoy3 19.05°N 111.05°E 186.7

下载: 导出CSV

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