Treffer: Ecology-informed symbolic machine learning: a methodological framework for classification of forest succession.

Ecology-informed symbolic machine learning: a methodological framework for classification of forest succession.

Bressane A; Environmental Engineering Department, Institute of Science and Technology (ICT), São José Dos Campos, Brazil. adriano.bressane@unesp.br.; Civil and Environmental Engineering Graduate Program, São Paulo State University (UNESP), Bauru, Brazil. adriano.bressane@unesp.br., Ewbank H; Civil Engineering Department, University of North Texas (UNT), Denton, USA., Negri RG; Environmental Engineering Department, Institute of Science and Technology (ICT), São José Dos Campos, Brazil.

Environmental monitoring and assessment [Environ Monit Assess] 2025 Nov 29; Vol. 197 (12), pp. 1386. Date of Electronic Publication: 2025 Nov 29.

Journal Article

English

Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE

Publication: 1998- : Dordrecht : Springer
Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-

Machine Learning* , Forests* , Environmental Monitoring*/methods, Brazil ; Ecology/methods

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Keywords: Allometric modeling; Ecological constraints; Forest monitoring; Machine learning interpretability; Succession classification

Date Created: 20251129 Date Completed: 20251129 Latest Revision: 20251210

20251210

10.1007/s10661-025-14836-3

41318828

MEDLINE