Forecasting transitions in the state of food security with machine learning using transferable features

Autor: Marc van den Homberg, Aklilu Teklesadik, Dennis L.J. van den Berg, Gabriela Guimarães Nobre, Joris J.L. Westerveld, Sjoerd Stuit

Publikováno v: Westerveld, J J L, van den Homberg, M J C, Nobre, G G, van den Berg, D L J, Teklesadik, A D & Stuit, S M 2021, ' Forecasting transitions in the state of food security with machine learning using transferable features ', Science of the Total Environment, vol. 786, 147366, pp. 1-15 . https://doi.org/10.1016/j.scitotenv.2021.147366
Science of the Total Environment, 786:147366, 1-15. Elsevier

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the act

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0229f84d7b19570ef433910021f2a27b
https://pubmed.ncbi.nlm.nih.gov/33971600