Machine‐learning based spatiotemporal prediction of soil moisture in a grassland hillslope
Bibliographic record
Abstract
Abstract Soil moisture (SM) plays a significant role in the earth's water balance and in optimizing land management practices. However, SM at the field scale is difficult to map from available point measurements due to the inherent heterogeneity of soil and terrain properties and temporal dynamics of weather conditions. In this study, we explored the potential of four machine learning (ML) methods (random forest, gradient boosted regression trees, support vector regression, and neural networks) to predict SM in a grassland hillslope in space and time using auxiliary variables on soil and terrain properties and weather conditions. For training and testing the ML models, we used SM point measurements obtained by a sensor network. Performance metrics varied between the ML methods and the training‐test data split ( R 2 = 0.48–0.69, root‐mean‐square error [RMSE] = 0.06–0.10). Random forests and gradient‐boosted regression trees turned out to be promising and easy to parametrize as first choices to explore the potential of ML techniques. The day of the year emerged as an important feature to predict SM across models and can thus serve as a proxy for seasonal hydroclimatic variability. To enable the transfer of the application to other contexts or sites, we provide the modeling workflow as an open‐source computational Python module.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".