Code supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2024
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| _version_ | 1866902131481509888 |
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| author | Kmoch, Alexander Harrison, Clay Taylor Uuemaa, Evelyn Choi, Jeonghwan |
| author_facet | Kmoch, Alexander Harrison, Clay Taylor Uuemaa, Evelyn Choi, Jeonghwan |
| contents | <p>Code supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon</p> <p>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14236923 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Code supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon Kmoch, Alexander Harrison, Clay Taylor Uuemaa, Evelyn Choi, Jeonghwan soil ml random forest <p>Code supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon</p> <p>Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa</p> <p>Spatial autocorrelation, the relationship between nearby samples of a spatial<br>random variable, is often overlooked in machine learning models, leading to<br>biased results. This study investigates various methods to account for spa-<br>tial autocorrelation when predicting soil organic carbon (SOC) using random<br>forest models. Five models incorporating spatial structure were compared<br>against baseline models that did not have any added spatial components.<br>Cross-validation showed slight improvements in accuracy for models consid-<br>ering spatial autocorrelation, while Shapley Additive Explanations confirmed<br>the importance of spatial variables. However, no decrease in spatial autocor-<br>relation of residuals was observed. Raster-based models exhibited enhanced<br>prediction detail, but high-resolution validation data availability limited thor-<br>ough validation. The findings emphasize the value of incorporating spatial<br>autocorrelation for improved SOC prediction in machine learning models.<br>Considerations such as the distribution of predictions and computational<br>complexity should help guide the selection of suitable approaches for specific<br>spatial modelling tasks.</p> |
| title | Code supplement: Spatial autocorrelation in machine learning for modelling soil organic carbon |
| topic | soil ml random forest |
| url | https://doi.org/10.5281/zenodo.14236923 |