季风转换对深圳地区呼吸系统疾病的影响及预测研究  

Study of the Influence of Monsoon Change on Respiratory Patients and the Prediction Model in Shenzhen Area

作　　者：吴千鹏 尹立[2] 李兴荣 孙羽[4] 黄开龙 苏春芳 王式功 WU Qianpeng;YIN Li;LI Xingrong;SUN Yu;HUANG Kailong;SU Chunfang;WANG Shigong(School of Atmospheric Sciences/Institute of Environmental Meteorology and Health,Chengdu University of Information Technology,Chengdu 610225,China;Meteorological Medical Research Center,Panzhihua Central Hospital,Panzhihua 617000,China;Shenzhen Meteorological Bureau,Shenzhen 518040,China;Climate Medicine Research Center,Second People's Hospital in Hainan Province,Wuzhi Shan 572299,China;Bureau of Meteorology of Shantou City,Shantou 515041,China)

机构地区：[1]成都信息工程大学大气科学学院/环境气象与健康研究院,四川成都610225 [2]攀枝花市中心医院气象医学研究中心,四川攀枝花617000 [3]深圳市气象局,广东深圳518040 [4]海南省第二人民医院气候医学研究中心,海南五指山572299 [5]汕头市气象局,广东汕头515041

出　　处：《沙漠与绿洲气象》2023年第6期32-40,共9页Desert and Oasis Meteorology

基　　金：攀枝花市科学技术局创新中心建设项目(2021ZX-5-1);2021年省级科技计划转移支付专项资金项目(21ZYZF-S-01);中国气象局西南区域气象中心创新团队基金(XNQYCXTD-202203);2021年度第二批攀枝花市市级科技计划项目(2021CY-S-4)。

摘　　要：利用深圳地区2015—2016年呼吸系统疾病就诊人数资料及同期气象和污染物资料,并运用BP人工神经网络和LSTM网络构建呼吸系统疾病就诊人数预测模型。结果显示:每年9月开始,冬季风的冷胁迫效应使相关人群呼吸系统疾病发病人数波动式增加,直至次年冬季风向夏季风转换前的3月发病人数达到峰值;夏季风控制期间当地居民呼吸系统疾病发病人数呈波动式减少,比峰值期间减少35%;在不同季风控制期间不同呼吸系统疾病其主控因素也不相同;对比两种预测模型,总体上LSTM网络预报模型对深圳下呼吸道疾病风险预测准确率更高,可以满足健康气象预报服务业务需求。This paper used the data on the number of patients with respiratory disease、meteorological factors and pollutant concentration from 2015 to 2016 to construct a prediction model for the admission visits from respiratory disease by using BP artificial neural network and LSTM network.The results showed that the cold stress effect of winter monsoon would increase the incidence of respiratory diseases in related populations fluctuating from September to the peak of March before the transition from winter wind to summer wind in the following year.During the summer wind control period,the incidence of respiratory diseases among local residents fluctuated and decreased,which was 35%lower than that during the peak period.In addition,the main control factors of respiratory diseases were different.Compared with the two prediction models,on the whole,the LSTM network forecasting model has a higher accuracy rate in predicting the risk of respiratory diseases in Shenzhen,which can meet the business needs of health weather forecasting services.

关 键 词：季风转换 呼吸系统疾病 气象与污染条件 人工神经网络 LSTM网络 预测模型 

分 类 号：P49[天文地球—大气科学及气象学]