气候变化领域集成服务门户(CCPortal): Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia

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DOI	10.3390/atmos15010043
	Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia
	Yang, Wenjing; Doulabian, Shahab; Toosi, Amirhossein Shadmehri; Alaghmand, Sina
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	1
卷号	15 期号:1
英文摘要	Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world's driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley-Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2 ' s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia.
英文关键词	meteorological drought; SPEI; potential evapotranspiration; SHAP; machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001148865600001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299353
作者单位	Tsinghua University; China Institute of Water Resources & Hydropower Research; Flinders University South Australia; Shahrood University of Technology; Monash University
推荐引用方式 GB/T 7714	Yang, Wenjing,Doulabian, Shahab,Toosi, Amirhossein Shadmehri,et al. Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia[J],2024,15(1).
APA	Yang, Wenjing,Doulabian, Shahab,Toosi, Amirhossein Shadmehri,&Alaghmand, Sina.(2024).Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia.ATMOSPHERE,15(1).
MLA	Yang, Wenjing,et al."Unravelling the Drought Variance Using Machine Learning Methods in Six Capital Cities of Australia".ATMOSPHERE 15.1(2024).