Article
14 November 2018
Published
10.1016/j.agrformet.2018.10.022
S0168192318303460
0168-1923
QA76 Computer software
006.3 Artificial intelligence
EPSRC Engineering and Physical Sciences Research Council; ANR, Agrobiosphere, France, project “FOR-WIND”; INRA scientific package awarded
Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. (2019). Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265, 16-29. https://doi.org/10.1016/j.agrformet.2018.10.022
ProfessorSchool of Computing Engineering and the Built Environment
0131 455 2783
E.Hart@napier.ac.uk
LecturerSchool of Computing Engineering and the Built Environment
0131 455 2497
K.Sim@napier.ac.uk
Machine learning; forest damage; wind risk, risk models, GALES, forest planning
2MB