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DOI	10.1016/j.eswa.2022.119056
	Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications
	Chakraborty, Debaditya; Basagaoglu, Hakan; Alian, Sara; Mirchi, Ali; Moriasi, Daniel N.; Starks, Patrick J.; Verser, Jerry A.
通讯作者	Chakraborty, D
来源期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
EISSN	1873-6793
出版年	2023
卷号	213
英文摘要	We present Multiscale Extrapolative Learning Algorithm (MELA) as a novel artificial-intelligence (AI)-based data extrapolator. MELA is capable of extending temporally limited local hydroclimatic measurements at fine spatial resolution to longer periods, using remotely-sensed hydroclimatic data readily available for longer periods but at coarse spatial resolution. We demonstrate the implementation of MELA to extrapolate the monthly local soil moisture measurements at multiple depths from 2015-2021 to 1958-2021 in a semi-arid region. Such data extrapolators are imperative to generate longer historical data needed to adequately train and test AI models while enhancing the chance of capturing the effects of extreme climates on spatially variable soil moisture. The MELA-extrapolated local soil moisture subsequently allowed the construction of monthly time-series of field-scale soil moisture distributions with a normalized accuracy of 72% and prediction of countywide annual winter wheat yields - using MELA-extrapolated soil moisture data and eXplainable AI (XAI) - with a normalized accuracy of 81%. Furthermore, the XAI model ranked the predictors based on their importance in estimating winter wheat yields, in which the soil moisture near the surface and in the root zone and precipitation totals were found to be more influential than temperature on crop yields in the semi-arid region. The XAI model also unveiled the inflection points of the predictors beyond which crop yields would increase or decrease. Moreover, the AI-based analyses in conjunction with climate projections from global climate models suggest potential reductions in rainfed crop yields in the study area by 2050 and 2100 in the absence of climate-resilient mitigation and adaptation plans.
英文关键词	Artificial Intelligence Data generator Soil moisture analysis Crop yield analysis Climate change impacts
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000890563300001
WOS关键词	CLIMATE-CHANGE ; YIELD ; REGRESSION ; NETWORK ; AI
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396345
推荐引用方式 GB/T 7714	Chakraborty, Debaditya,Basagaoglu, Hakan,Alian, Sara,et al. Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications[J],2023,213.
APA	Chakraborty, Debaditya.,Basagaoglu, Hakan.,Alian, Sara.,Mirchi, Ali.,Moriasi, Daniel N..,...&Verser, Jerry A..(2023).Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications.EXPERT SYSTEMS WITH APPLICATIONS,213.
MLA	Chakraborty, Debaditya,et al."Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications".EXPERT SYSTEMS WITH APPLICATIONS 213(2023).