首页|期刊导航|国际水土保持研究(英文)|Novel deep learning algorithm in soil erodibility factor predicting at a continental scale
国际水土保持研究(英文)Issue(1):300-321,22.DOI:10.1016/j.iswcr.2025.09.008
Novel deep learning algorithm in soil erodibility factor predicting at a continental scale
Novel deep learning algorithm in soil erodibility factor predicting at a continental scale
摘要
关键词
Soil erodibility/Land management/Sustainable development/Agricultural productivity/Machine learningKey words
Soil erodibility/Land management/Sustainable development/Agricultural productivity/Machine learning引用本文复制引用
Ataollah Shirzadi,Himan Shahabi,Maryam Rahimzad,Aryan Salvati,Abolfazl Jaafari,Victoria Kress,Panos Panagos..Novel deep learning algorithm in soil erodibility factor predicting at a continental scale[J].国际水土保持研究(英文),2026,(1):300-321,22.基金项目
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. (EUROSTAT)