A review of machine learning approaches to soil temperature estimation

Par Conseil national de recherches du Canada

Téléchargement	Voir la version finale : A review of machine learning approaches to soil temperature estimation (PDF, 1.2 Mio)
DOI	Trouver le DOI : https://doi.org/10.3390/su15097677
Auteur	Rechercher : Taheri, Mercedeh¹; Rechercher : Schreiner, Helene Katherine; Rechercher : Mohammadian, Abdolmajid Identifiant ORCID : https://orcid.org/0000-0001-5381-8189; Rechercher : Shirkhani, Hamidreza²; Rechercher : Payeur, Pierre Identifiant ORCID : https://orcid.org/0000-0003-3103-9752; Rechercher : Imanian, Hanifeh Identifiant ORCID : https://orcid.org/0000-0001-5022-1993; Rechercher : Cobo, Juan Hiedra Identifiant ORCID : https://orcid.org/0000-0002-0849-4530
Affiliation	Conseil national de recherches du Canada. Herzberg en astronomie et en astrophysique Conseil national de recherches du Canada. Construction
Format	Texte, Article
Sujet	soil temperature; direct measurement; empirical methods; physical models; machine learning
Résumé	Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.
Date de publication	2023-05-07
Maison d’édition	MDPI
Licence	Creative Commons, Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	Sustainability 15, nº 9, 7677 (7 mai 2023).
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
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Identificateur de l’enregistrement	6cc3f228-2350-45e0-83b0-6fbc0579d29f
Enregistrement créé	2024-03-13
Enregistrement modifié	2024-03-13

Date de modification :: 2024-07-02