气候变化领域集成服务门户(CCPortal): Exploring machine learning potential for climate change risk assessment

CCPortal

DOI	10.1016/j.earscirev.2021.103752
	Exploring machine learning potential for climate change risk assessment
	Zennaro F.; Furlan E.; Simeoni C.; Torresan S.; Aslan S.; Critto A.; Marcomini A.
发表日期	2021
ISSN	00128252
卷号	220
英文摘要	Global warming is exacerbating weather, and climate extremes events and is projected to aggravate multi-sectorial risks. A multiplicity of climate hazards will be involved, triggering cumulative and interactive impacts on a variety of natural and human systems. An improved understanding of risk interactions and dynamics is required to support decision makers in their ability to better manage current and future climate change risks. To face this issue, the research community has been starting to test new methodological approaches and tools, including the application of Machine Learning (ML) leveraging the potential of the large availability and variety of spatio-temporal big data for environmental applications. Given the increasing attention on the application of ML methods to Climate Change Risk Assessment (CCRA), this review mapped out the state of art and potential of these methods to this field of research. Scientometric and systematic analysis were jointly applied providing an in-depth review of publications across the 2000–2020 timeframe. The resulting output from the analysis showed that a huge variety of ML algorithms have been already applied within CCRA, among them, the most recurrent are Decision Tree, Random Forest, and Artificial Neural Network. These algorithms are often applied in an ensemble or hybridized way to analyze most of all floods and landslides risk events. Moreover, the application of ML to deal with remote sensing data is consistent and effective across reviewed CCRA applications, allowing the identification and classification of targets and the detection of environmental and structural features. On the contrary concerning future climate change scenarios, literature seems not to be very widespread into scientific production, compared to studies evaluating risks under current conditions. The same lack can be noted also for the assessment of cascading and compound hazards and risks, since these concepts are recently emerging in CCRA literature but not yet in combination with ML-based applications. © 2021 Elsevier B.V.
关键词	Big data Climate change risk assessment Machine learning Remote sensing Scientometric analysis Systematic review
语种	英语
来源期刊	Earth Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/204181
作者单位	Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, Venice, I-30170, Italy; Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, I-73100, Italy; International Computer Institute, Ege University, Bornova, Izmir 35100, Turkey; European Centre for Living Technology (ECLT), Ca’ Foscari University of Venice, Dorsoduro 3911, Calle Crosera, Venice, 30123, Italy
推荐引用方式 GB/T 7714	Zennaro F.,Furlan E.,Simeoni C.,et al. Exploring machine learning potential for climate change risk assessment[J],2021,220.
APA	Zennaro F..,Furlan E..,Simeoni C..,Torresan S..,Aslan S..,...&Marcomini A..(2021).Exploring machine learning potential for climate change risk assessment.Earth Science Reviews,220.
MLA	Zennaro F.,et al."Exploring machine learning potential for climate change risk assessment".Earth Science Reviews 220(2021).