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Project properties |
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| Title | Digital Soil Mapping: Random Forest spatial interpolation |
| Group | Soil Geography and Landscape |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Gerard Heuvelink |
| Examiner(s) | Gerard Heuvelink |
| Contact info | gerard.heuvelink@wur.nl |
| Begin date | 2022/01/03 |
| End date | |
| Description | The thesis research will begin with a literature review of machine learning for soil mapping. Next you will study the RFSI algorithm and apply it to a test dataset. This is all done in R, using existing scripts that need slight modification. Once this is completed you will test the method for a real-world digital soil mapping case study. This involves the selection of a study area and preparing covariates and soil point observations, provided by ISRIC - World Soil Information. Application to the case study must also include a cross-validation of results, comparison with conventional random forest, and optimisation of hyperparameters, such as the number of nearest observations that should be included as covariates. The results of this MSc research will likely be very useful to the ISRIC SoilGrids project (www.soilgrids.org). |
| Used skills | Statistics, machine learning, R scripting, GIS |
| Requirements | - Solid background in statistical modelling, such as obtained through the Spatial Modelling and Statistics course
- Experience with programming in R |