Novel deep learning algorithm in soil erodibility factor predicting at a continental scaleOA
Novel deep learning algorithm in soil erodibility factor predicting at a continental scale
Ataollah Shirzadi;Himan Shahabi;Maryam Rahimzad;Aryan Salvati;Abolfazl Jaafari;Victoria Kress;Panos Panagos
Department of Rangeland and Watershed Management,Faculty of Natural Resources,University of Kurdistan,Sanandaj,IranDepartment of Geomorphology,Faculty of Natural Resources,University of Kurdistan,Sanandaj,IranCenter Eau Terre Environnement,Institut National de la Recherche Scientifique(INRS),Quebec City,QC,G1K 9A9,Canada||Ottawa Research and Development Centre,Agriculture and Agri-Food Canada,960 Carling Avenue Ottawa,ON,K1A0C6,CanadaDepartment of Arid and Mountainous Regions Reclamation,Faculty of Natural Resources,University of Tehran,Karaj,IranResearch Institute of Forests and Rangelands,Agricultural Research,Education and Extension Organization(AREEO),Tehran,IranMinistry of Forests,British Columbia,CanadaEuropean Commission,Joint Research Centre(JRC),Ispra,Italy
Soil erodibilityLand managementSustainable developmentAgricultural productivityMachine learning
Soil erodibilityLand managementSustainable developmentAgricultural productivityMachine learning
《国际水土保持研究（英文）》 2026 (1)
300-321,22
The LUCAS Survey is coordinated by Unit E4 of the Statistical Office of the European Union(EUROSTAT).The collection of LUCAS soil samples and subsequent laboratory analyses are supported by the Directorate-General for Agriculture and Rural Development(DG-AGRI),the Directorate-General for Climate Action(DG-CLIMA),and the Directorate-General for Environment(DG-ENV).This research was funded by the University of Kurdistan,Iran(Grant Nos.02-09-18977 and 02-09-10394).The authors would also like to express their sincere gratitude to Dr.Marten Geertsema for editing and revising the English language of the initial manu-script version.
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