摘要
本文基于广东汕头市统计局提供的社会经济发展指标数据和气象观测数据,利用主成分分析法、B-P神经网络和C5.0决策树算法,对广东汕头市“热岛效应”的年际、季节和日变化尺度特征进行了分析。研究发现,汕头市年际尺度上近十年均呈现“热岛效应”,季节尺度上热岛强度夏季强于冬季,日变化尺度上在中午前后较强。同时发现,影响汕头市“热岛效应”的主要因子为绿化覆盖率、建成区面积和气压。绿化覆盖率通过植物的蒸腾作用吸热减弱热岛强度,建成区面积通过城区下垫面储热性质变化和“屋顶低压效应”增强热岛强度,气压通过“热岛环流”减弱热岛强度。通过对“雨岛效应”和“浑浊岛效应”分析发现,汕头市“雨岛效应”具有增强的趋势,并主要出现在夏季。“浑浊岛效应”呈现先增强后减弱趋势,秋季和冬季较强,春季和夏季较弱。
The annual,seasonal and daily scale characteristics of“Urban Heat Island Effect(UHI)”of Shantou are analyzed using the social and economic development index and meteorological data.The main factors affecting the UHI are examined using the artificial intelligence algorithms including the principal component analysis,B-P neural network and C5.0 decision tree algorithm.The results indicate that the UHI increased generally during the last ten years in annual scale and its intensity is stronger in summer than that in winter in seasonal scale.In daily scale,the UHI is the strongest around the midday.The green coverage rate,construction land area and air pressure are main factors impacting the UHI.The green coverage rate weakens the UHI through the plant transpiration and heat absorption,the construction land area enhances the UHI by changing the heat storage properties of the underlying surface and the“Roof Low Pressure Effect”,and the air pressure weakens the UHI through the“Heat Island Circulation”.The“Urban Rain Island Effect”of Shantou increases during recent years and mainly appeared in summer.The“Urban Turbid Island Effect”has a tendency to increase and then decrease in the annual scale and it is stronger in autumn and winter than in spring and summer.The study can provide reference for the scientific planning of the city development in Shantou and the Western Taiwan Straits Economic Zone Urban agglomeration.
作者
张树钦
黄哲帆
唐若莹
赖鹏有
张韶晶
陶咪咪
Zhang Shuqin;Huang Zhefan;Tang Ruoying;Lai Pengyou;Zhang Shaojing;Tao Mimi(College of Ocean and Meteorology,Guangdong Ocean University,Zhanjiang 524088,China;South China Sea Institute of Marine Meteorology,Guangdong Ocean University,Zhanjiang 524088,China;CMA-GDOU Joint Laboratory for Marine Meteorology,Zhanjiang 524088,China;Key Laboratory of Climate,Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province,Guangdong Ocean University,Zhanjiang 524088,China;College of Coastal Agricultural Sciences,Guangdong Ocean University,Zhanjiang 524088,China)
出处
《中国海洋大学学报(自然科学版)》
CAS
CSCD
北大核心
2022年第8期19-30,共12页
Periodical of Ocean University of China
基金
广东海洋大学“创新强校工程”科研项目(230419106)
广东海洋大学本科生创新团队项目(010404032101)
广东海洋大学科研启动费资助项目(R19045)
2020年度广东省大学生创新创业训练计划项目(580520052)
国家自然科学基金项目(42075036)资助。
关键词
热岛效应
统计分析
人工智能
城市气象
urban heat island effect
statistical analysis
artificial intelligence
urban meteorology