文献类型：期刊文献

中文题名：基于ConvLSTM机器学习的风暴潮漫滩预报研究

英文题名：Research on storm surge floodplain prediction based on ConvLSTM machine learning

作者：谢文鸿[1];徐广珺[2,3];董昌明[1,2]

机构：[1]南京信息工程大学海洋数值模拟与观测实验室,江苏南京210044;[2]南方海洋科学与工程广东省实验室(珠海),广东珠海519000;[3]广东海洋大学电子与信息工程学院,广东湛江524088

年份：2022

卷号：45

期号：5

起止页码：674

中文期刊名：大气科学学报

外文期刊名：Transactions of Atmospheric Sciences

收录：CSTPCD、、北大核心、CSCD、北大核心2020、CSCD_E2021_2022

基金：南方海洋科学与工程广东省实验室(珠海)资助项目(SML2020SP007);江苏省自然资源发展专项资金(海洋科技创新)项目(JSZRHYKJ202102);广东省海洋经济发展(海洋六大产业)专项资金项目(粤自然资合[2020]049)

语种：中文

中文关键词：风暴潮;ConvLSTM;机器学习;数据驱动预报

外文关键词：storm surge;ConvLSTM;machine learning;data-driven forecast

中文摘要：风暴潮是指由强烈的大气扰动所导致的海面异常升高现象,由热带气旋引起的风暴潮常对沿海地区造成巨大的社会经济、人类活动和生命财产危害。依靠数据驱动的强非线性映射能力的机器学习方法较传统数值模式预报在耗费研究资源和计算时间上更具优势。本文选取广东省珠江口为研究区域,基于卷积长短时记忆网络(Convolutional LSTM network, ConvLSTM)机器学习算法展开风暴潮漫滩预报研究,利用由再分析资料驱动的数值模式产品构建了历史台风漫滩数据集,用于机器学习模型训练、验证和测试。研究了两种预报方式,一种是基于海表面高度场的自回归预报,另一种是依赖预报风场和初始海表面高度场进行的预报;它们可以实现基于数据驱动的风暴潮漫滩预报,其中自回归预报模型表现更优。相较于传统动力学数值预报,基于数据驱动的ConvLSTM预报模型结构更为轻便,所需驱动数据更少,在缺少边界条件、地形、径流等信号时,在短临预报中仍能基本复现数值模式模拟的结果。

外文摘要：A storm surge is the anomalous rising of the sea surface induced by intense atmospheric disturbances.Storm surges caused by tropical cyclones often cause great socio-economic,human activity and life and property hazards to coastal areas.Therefore,realizing accurate and timely storm surge floodplain prediction is critical.Numerical models are currently the primary method used to predict storm surges,and high-resolution floodplain models always need a significant investment in both research funds and processing time.The machine learning approach,which depends on the robust nonlinear mapping capability driven by data,has an edge over the conventional numerical model prediction in terms of research time and computational resource consumption.This paper uses the convolutional long-short term memory network (ConvLSTM) machine learning algorithm to predict storm surge floodplain in the Pearl River Estuary in Guangdong Province.Using the numerical model products driven by reanalysis data,the historical typhoon floodplain data set is constructed for machine learning model training,verification and testing.The paper studies two prediction techniques including the autoregressive prediction based on the sea surface height field and the prediction based on the predicted wind field and initial sea surface height field,which may realize the storm surge floodplain forecast based on data-driven scheme.Among them,the autoregressive prediction model performs better.By testing the previous model,it concludes that ConvLSTM can predict floodplains with a general error of less than 0.2 m based on the sea surface height field a few hours ago,even if the boundary conditions,topography,surface runoff and atmospheric signals are unknown.Under such conditions,the larger errors mostly occur at the coast and on both sides of the river.By analyzing the errors of the two models,it finds that adding wind field input to ConvLSTM does not significantly improve the prediction skills of the model.Further studies are required to determine the better way to train the data-driven prediction model by adding more features.