干旱区生态环境知识资源中心(Arid): Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney

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DOI	10.1016/j.scitotenv.2019.136068
	Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney
	Yun, Geun Young 1; Ngarambe, Jack 1; Duhirwe, Patrick Nzivugira 1; Ulpiani, Giulia 2; Paolini, Riccardo 3; Haddad, Shamila 3; Vasilakopoulou, Konstantina 3; Santamouris, Mat 1,3
通讯作者	Ulpiani, Giulia
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2020
卷号	709
英文摘要	The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R-2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized. (C) 2019 Elsevier B.V. All rights reserved.
英文关键词	Urban heat island LSTM Sea breeze Desert winds, AI forecasting models Regional climate change Synoptic conditions
类型	Article
语种	英语
国家	South Korea ; Australia
收录类别	SCI-E
WOS记录号	WOS:000512281700056
WOS关键词	SHORT-TERM-MEMORY ; AMBIENT AIR-TEMPERATURE ; NEURAL-NETWORK ; LOCAL CLIMATE ; ENERGY DEMAND ; INTENSITY ; ATHENS ; BUILDINGS ; ENSEMBLE ; SURFACE
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315512
作者单位	1.Kyung Hee Univ, Dept Architectural Engn, 1 Seocheon Dong, Yongin 446701, Gyeonggi Do, South Korea; 2.Univ Sydney, Sch Civil Engn, Bldg J05,Room 260, Sydney, NSW 2006, Australia; 3.Univ New South Wales, Fac Built Environm, Sydney, NSW, Australia
推荐引用方式 GB/T 7714	Yun, Geun Young,Ngarambe, Jack,Duhirwe, Patrick Nzivugira,et al. Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney[J],2020,709.
APA	Yun, Geun Young.,Ngarambe, Jack.,Duhirwe, Patrick Nzivugira.,Ulpiani, Giulia.,Paolini, Riccardo.,...&Santamouris, Mat.(2020).Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney.SCIENCE OF THE TOTAL ENVIRONMENT,709.
MLA	Yun, Geun Young,et al."Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney".SCIENCE OF THE TOTAL ENVIRONMENT 709(2020).