论文标题
预测热带风暴的贝叶斯间隔
Prediction of Bayesian Intervals for Tropical Storms
论文作者
论文摘要
在使用复发性神经网络（RNN）预测飓风轨迹的最新研究的基础上，我们开发了改进的方法，并概括了预测贝叶斯间隔的方法，除了简单的点估计外。热带风暴能够造成严重损害，因此准确地预测其轨迹会给城市和生活带来重大利益，尤其是由于气候变化的影响，它们变得更加强烈。通过在RNN中使用辍学的贝叶斯间隔，我们可以提高预测的可行性，例如，通过估计降级区域的区域。我们使用RNN以6小时的间隔预测风暴的轨迹。我们使用了大西洋约500次热带风暴的统计飓风强度预测方案（SHIPS）数据集的纬度，经度，风速和压力特征。我们的结果表明了神经网络辍学值如何影响预测和间隔。
Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict Bayesian intervals in addition to simple point estimates. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating the areas to evacuate in the landfall region. We used an RNN to predict the trajectory of the storms at 6-hour intervals. We used latitude, longitude, windspeed, and pressure features from a Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic Ocean. Our results show how neural network dropout values affect predictions and intervals.
