| 基于机器学习的海洋浮标寿命及轨迹预测 |
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| 引用本文： | 陈若冰,陈萱,董明媚,梁建峰,李雨森,王刚,刘晓光.基于机器学习的海洋浮标寿命及轨迹预测[J].海洋通报,2021,40(3). |
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| 作者姓名： | 陈若冰 陈萱 董明媚 梁建峰 李雨森 王刚 刘晓光 |
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| 作者单位： | 南开大学计算机学院,天津300350;国家海洋信息中心,天津300171;自然资源部海洋信息技术创新中心,天津300171 |
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| 基金项目： | 自然资源部海洋信息技术创新中心 2018 年度开放基金 |
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| 摘 要： | Argo计划(Array or Real-time Geostrophic Oceanography)为海洋和大气研究提供了宝贵的资料,在短期天气预报和长期气候预测中起到了重要作用。为保证Argo观测阵列的正常运转,需要时刻关注浮标的运行状态,以保证研究区域内维持一定数量和密度的浮标。然而Argo浮标投放费用高昂,投放过早会导致资源浪费,投放过迟会导致信息资料的缺失。本文旨在使用机器学习的方法对Argo浮标在未来某个时间点的位置和状态(仍在工作或已经损坏)进行预测,以提前制定投放计划,保证在正确的位置和时间投放新的浮标,以减少资金投入。对于浮标寿命预测任务,除硬件特征之外添加额外的已存活时间作为动态属性,使用回归决策树、梯度提升回归树、随机森林和支持向量回归机等机器学习方法,对浮标剩余寿命进行预测。对于浮标轨迹预测任务,使用基于LSTM的Encoder-Decoder模型对未来多个时间步后的浮标的经/纬度信息进行预测,有效地避免了传统的LSTM模型循环单步预测所带来的误差累积问题。实验证明本文提出的浮标剩余寿命和位置预测模型都能达到较高的预测准确率,对指导浮标投放有重要意义。
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| 关 键 词： | Argo数据 寿命预测 轨迹预测 机器学习 |
| 收稿时间： | 2020/9/21 0:00:00 |
| 修稿时间： | 2021/1/7 0:00:00 |
Lifetime and trajectory prediction of marine floats based on machine learning methods |
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| CHEN Ruobing,CHEN Xuan,DONG Mingmei,LIANG Jianfeng,LI Yusen,WANG Gang,LIU Xiaoguang.Lifetime and trajectory prediction of marine floats based on machine learning methods[J].Marine Science Bulletin,2021,40(3). |
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| Authors: | CHEN Ruobing CHEN Xuan DONG Mingmei LIANG Jianfeng LI Yusen WANG Gang LIU Xiaoguang |
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| Institution: | College of Computer Science, Nankai University, Tianjin 300350, China;National Marine Data and Information Service, Tianjin 300171, China;Marine Information Technology Innovation Center, Ministry of Natural Resources, Tianjin 300171, China |
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| Abstract: | The Argo (Array or Real-time Geostrophic Oceanography) provides valuable data for ocean and atmospheric research, and plays an important role in short-term weather forecasting and long-term climate prediction. In order to ensure the normal operation of the Argo array, it is necessary to pay attention to the operation status of the floats at all times to ensure that a certain number and density of floats are maintained in the study area. The replacement cost of Argo floats is high. Too early release will result in waste of resources and too late release will result in the lack of information. This article aims to use machine learning methods to predict the position and status of Argo floats at a certain point in the future (still working or damaged), in order to formulate a delivery plan in advance to ensure that new floats are placed at the correct location and time and reduce capital investment. In the float life prediction, additional survival time is added as a dynamic attribute besides hardware features. Machine learning methods such as Decision Tree Regressor, Gradient Boosting Regression Tree, Random Forest and Support Vector Regression are used to predict the remaining life of the floats. For the float trajectory prediction, the Encoder-Decoder model based on LSTM is used to predict the latitude/longitude information of floats for multiple time steps in the future, which effectively avoids the error accumulation caused by the traditional LSTM model cyclic single-step prediction. The experiment proves that the float remaining life and position prediction model proposed in this paper can achieve high prediction accuracy, thus is of great significance to guide the float delivery. |
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| Keywords: | Argo data life prediction trajectory prediction machine learning |
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