Source: Journal of Arid Environments. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DEGRADAÇÃO DO SOLO, MODELAGEM DE DADOS, QUALIDADE DA ÁGUA, SENSORIAMENTO REMOTO, SOLO ÁRIDO
ABNT
DERAKHSHAN-BABAEI, Farzaneh et al. Assessment of land degradability and water quality in a semiarid region: machine learning approach and multi scale analysis. Journal of Arid Environments, v. 234, p. 1-16, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.jaridenv.2026.105562. Acesso em: 21 abr. 2026.APA
Derakhshan-Babaei, F., Darvishi Boloorani, A., Ahmadi Mirghaed, F., Alavi, S. J., Demattê, J. A. M., & Huang, K. (2026). Assessment of land degradability and water quality in a semiarid region: machine learning approach and multi scale analysis. Journal of Arid Environments, 234, 1-16. doi:10.1016/j.jaridenv.2026.105562NLM
Derakhshan-Babaei F, Darvishi Boloorani A, Ahmadi Mirghaed F, Alavi SJ, Demattê JAM, Huang K. Assessment of land degradability and water quality in a semiarid region: machine learning approach and multi scale analysis [Internet]. Journal of Arid Environments. 2026 ; 234 1-16.[citado 2026 abr. 21 ] Available from: https://doi.org/10.1016/j.jaridenv.2026.105562Vancouver
Derakhshan-Babaei F, Darvishi Boloorani A, Ahmadi Mirghaed F, Alavi SJ, Demattê JAM, Huang K. Assessment of land degradability and water quality in a semiarid region: machine learning approach and multi scale analysis [Internet]. Journal of Arid Environments. 2026 ; 234 1-16.[citado 2026 abr. 21 ] Available from: https://doi.org/10.1016/j.jaridenv.2026.105562
