Published in Ecological Informatics, 2025
Importance Ranking of Modelling Choices in Quantile Regression Forest-Based Spatial Predictions When Data are Sparse, Imprecise and Clustered
Recommended citation: Rohmer, J. (2025). Importance Ranking of Modelling Choices in Quantile Regression Forest-Based Spatial Predictions When Data are Sparse, Imprecise and Clustered; Ecological Informatics. Submitted https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5095837
Published in Lecture Notes in Networks and Systems, 2024
Recommended citation: Labourg, P., Destercke, S., Guillaume, R., Rohmer, J., Quost, B., Belbèze, S. (2024). Geospatial Uncertainties: A Focus on Intervals and Spatial Models Based on Inverse Distance Weighting. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2024. Lecture Notes in Networks and Systems, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-031-74003-9_30 https://doi.org/10.1007/978-3-031-74003-9_30
Published in SOIL, 2024
Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach
Recommended citation: Rohmer, J., Belbeze, S., and Guyonnet, D.: Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach, SOIL, 10, 679–697, https://doi.org/10.5194/soil-10-679-2024, 2024. https://soil.copernicus.org/articles/10/679/2024/
Published in Journal of Geochemical Exploration, 2023
A literature review of methods used to establish urban soil geochemical background
Recommended citation: Belbèze, S., Rohmer, J., Négrel, P., Guyonnet, D. (2023). Defining urban soil geochemical backgrounds: A review for application to the French context; Journal of Geochemical Exploration. 254, 107298. https://www.sciencedirect.com/science/article/abs/pii/S0375674223001450