12 citations as recorded by crossref.

1. The site‐specific selection of the infiltration model based on the global dataset and random forest algorithm S. Kim et al. 10.1002/vzj2.20125
2. Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation A. Gebauer et al. 10.3389/fsoil.2021.770326
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4. Method of pedotransfer modeling of soil moisture supply on sloping lands V. Belolipsky et al. 10.31073/acss92-01
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6. Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling N. Dang et al. 10.3897/oneeco.7.e76410
7. Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
8. Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning L. Laurence et al. 10.1016/j.geoderma.2023.116692
9. Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils A. Sakhaee et al. 10.3390/agriculture14081298
10. Hydro-pedotransfer functions: a roadmap for future development T. Weber et al. 10.5194/hess-28-3391-2024
11. Pedotransfer functions and machine learning: Advancements and challenges in tropical soils L. de Castro Moreira da Silva et al. 10.1016/j.geodrs.2023.e00720
12. Development of pedotransfer functions for water retention in tropical mountain soil landscapes: spotlight on parameter tuning in machine learning A. Gebauer et al. 10.5194/soil-6-215-2020