2023/03/08 by Luca Patelli, Patelli, Luca, Michela Cameletti +5 · 1 citation
Agricultural and Biological Sciences · Environmental Science · #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Remote Sensing in Agriculture #Smart Agriculture and AI #Sustainable Agricultural Systems Analysis
paper · pdf · doi:10.48550/arxiv.2303.04693
openalex publication_date 2023/03/08 · openalex created_date 2023/03/11 · openalex updated_date 2026/07/28
Random Forest (RF) is a well-known data-driven algorithm applied in several fields thanks to its flexibility in modeling the relationship between the response variable and the predictors, also in case of strong non-linearities. In environmental applications, it often occurs that the phenomenon of interest may present spatial and/or temporal dependence that is not taken explicitly into account by RF in its standard version. In this work, we propose a taxonomy to classify strategies according to when (Pre-, In- and/or Post-processing) they try to include the spatial information into regression RF. Moreover, we provide a systematic review and classify the most recent strategies adopted to "adjust" regression RF to spatially dependent data, based on the criteria provided by the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA). The latter consists of a reproducible methodology for collecting and processing existing literature on a specified topic from different sources. PRISMA starts with a query and ends with a set of scientific documents to review: we performed an online query on the 25th October 2022 and, in the end, 32 documents were considered for review. The employed methodological strategies and the application fields considered in the 32 scientific documents are described and discussed. This work falls inside the Agriculture Impact On Italian Air (AgrImOnIA) project.