Artificial intelligence and Eddy covariance: A review.

Autor: Lucarini A; Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; University School for Advanced Studies IUSS Pavia, Palazzo del Broletto, Piazza della Vittoria 15, 27100 Pavia, Italy. Electronic address: arianna.lucarini@iusspavia.it., Cascio ML; Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Italy., Marras S; Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Italy., Sirca C; Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Italy; National Biodiversity Future Center (NBFC), Palazzo Steri, Piazza Marina 61, Palermo 90133, Italy., Spano D; Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Italy; National Biodiversity Future Center (NBFC), Palazzo Steri, Piazza Marina 61, Palermo 90133, Italy.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Nov 10; Vol. 950, pp. 175406. Date of Electronic Publication: 2024 Aug 08.
DOI: 10.1016/j.scitotenv.2024.175406
Abstrakt: The Eddy Covariance (EC) method allows for monitoring carbon, water, and energy fluxes between Earth's surface and atmosphere. Due to its varying interdependent data streams and abundance of data as a whole, EC is naturally suited to Artificial Intelligence (AI) approaches. The integration of AI and EC will likely play a crucial role in the climate change mitigation and adaptation goals defined in the Sustainable Development Goals (SDGs) of the Agenda 2030. To aid this, we present a scoping review in which the novelty of various AI techniques in monitoring fluxes through the EC method from the past two decades has been collected. Overall, we find a clear positive trend in the quantity of research in this area, particularly in the last five years. We also find a lack of uniformity in available techniques, due to the diverse technologies and variables employed across environmental conditions and ecosystems. We highlight the most applied Machine Learning (ML) models, over the 71 algorithms identified in the scoping review, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Support Vector Regression (SVR), and K-Nearest Neigbor (KNN). We suggest that future progress in this field requires an international, collaborative effort involving computer scientists and ecologists. Modern Deep Learning (DL) techniques such as Transformers and generative AI must be investigated to find how they may benefit our field. A forward-looking strategy must be formed for the optimal utilization of AI combined with EC to define future actions in flux monitoring in the face of climate change.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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