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Received:November 14, 2018 Revised:December 10, 2018
Received:November 14, 2018 Revised:December 10, 2018
中文摘要: 长短期记忆网络（LSTM）是一种时间递归神经网络，适合于预测时间序列延续性相对较长的事件.本文基于LSTM网络构建了一个全新的大雾临近预报框架，首先将地面气象要素观测资料转化成时间序列数据，并基于此序列进行建模.为了验证提出的模型的准确性，将安徽省81个国家站近2年地面气象要素数据转换为序列数据，基于该数据集对未来1–4小时进行逐小时大雾预报实验，实验结果显示本文提出的模型其TS-Score分别为61%、55%、36%和31%，明显优于卷积神经网络（CNN）以及传统机器学习算法如支持向量机（SVM）和K-近邻算法（KNN）的预测结果，是大雾临近预报的一种有效预报方法.
Abstract:Long-Term and Short-Term Memory (LSTM) network is a time recursive neural network, which is suitable for predicting events with relatively long delay in time series. In this study, a new fog proximity prediction framework based on LSTM network is constructed, which can transform meteorological observation data into time series data and model them based on time series data. In order to validate the proposed model effectively, this study transforms the surface meteorological data of 81 national stations in Anhui Province from October 2015 to June 2017 into sequence data and constructs a validation data set. Based on this data set, the future 1-4 hourly fog forecasting experiments are carried out. The experimental results show that the proposed model's TS-Scores are 61%, 55%, 36%, and 31%, respectively, which are obviously better than CNN and those of traditional machine learning algorithms such as SVM and KNN. It is an effective method for fog prediction.
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基金项目:江苏省气象科学研究所北极阁基金（BJG201707）
引用文本：
苗开超,韩婷婷,王传辉,章军,姚叶青,周建平.基于LSTM网络的大雾临近预报模型及应用.计算机系统应用,2019,28(5):215-219
MIAO Kai-Chao,HAN Ting-Ting,WANG Chuan-Hui,ZHANG Jun,YAO Ye-Qing,ZHOU Jian-Ping.Fog Nowcasting Model Based on LSTM Network and Its Application.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):215-219
苗开超,韩婷婷,王传辉,章军,姚叶青,周建平.基于LSTM网络的大雾临近预报模型及应用.计算机系统应用,2019,28(5):215-219
MIAO Kai-Chao,HAN Ting-Ting,WANG Chuan-Hui,ZHANG Jun,YAO Ye-Qing,ZHOU Jian-Ping.Fog Nowcasting Model Based on LSTM Network and Its Application.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):215-219
