基于机器学习的海洋浮标寿命及轨迹预测

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...

Lifetime and trajectory prediction of marine floats based on machine learning methods

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.