Utility-based regression and meta-learning techniques for modeling actual ET:Comparison to(METRIC-EEFLUX)model  

作　　者：Fatima K.Abu Salem Sara Awad Yasmine Hamdar Samer Kharroubi Hadi Jaafar 

机构地区：[1]Department of Computer Science,Faculty of Arts and Sciences,American University of Beirut,Beirut,Lebanon [2]Department of Nutition and Food Sciences,Faculty of Agriculturaland Food Sciences,American University of Beirut,Beirut,Lebanon [3]Deparment of Agriculture,Faculty ofAgricultural and Food Sciences,American University of Beirut Beirut,Lebanon

出　　处：《Artificial Intelligence in Agriculture》2024年第4期43-55,共13页农业人工智能(英文)

摘　　要：Estimating actual evapotranspiration(ET.)is crucial for water resource management,yet existing methods face limitations.Traditional approaches,including eddy covariance and remote sensing-based energy balance methods,often struggle with high costs,limited spatial and temporal coverage,and reduced predictive accuracy,particularly for classical empirical models While machine learning has emerged as a promising alternative,it still presents challenges,notably in underestimating ET,during periods of high heat.We attribute this to insufficient learning on the rare but highly relevant ET,values of interest,or the not-so-big climatic datasets available for use.In this manuscript,we demonstrate how few-shot,meta-learning models(MAML)that are specifically designed for enhanced generalizability on not-so-big datasets can outperform basic machine learning models in upscaling ET.from two major in-situ towers,the Ameriflux and Euroflux.Using limited remotely sensed land surface data from the METRIC-EEFlux and limited climatic variables,we demonstrate that the chosen models can attain quantifiable utility within the utility-based-regression paradigm towards impactful practical considerations.Our initial explorations reveal that EEflux ET deviates significantly from in-situ observations measured through the Ameriflux and EEflux towers(R^(2)=39%)Instead,MAML shows best performance in approximating ET,than basic machine learning algorithms and EEFlux(R^(2)=71%on entire testing dataset,R?=0.88 on the Csa climate,R^(2)=0.79 on the Cfa climate,and R^(2)=0.78 on the CSH vegetation class),and continues to improve without overfitting even when exposed to a relatively small training dataset.Its high F2 score(96%)indicates that MAML has very high precision and recall for rare cases,which is significant for irrigation.Of independent interest,this study confirms that limited remotely sensed EEflux products contribute significantly to knowledge about ground truth ET,and can thus be of valuable use in settings where access to good quality an

关 键 词：EVAPOTRANSPIRATION Machine learning Artificial intelligence EEFlux 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]