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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20392497 |
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Table of Contents:
- <h2>First Release</h2> <p>This is the first official release of the Heteroscedastic Gaussian Process Regression (HGPR) model for soil pH prediction in reclaimed mine lands.</p> <h3>Key Results</h3> <ul> <li>R² = 0.886 predictive accuracy</li> <li>10.9% improvement in prediction interval calibration over standard GPR</li> <li>94.6% coverage in acidic zones (pH < 5.0) vs 86.1% for standard GPR</li> </ul> <h3>Model Features</h3> <ul> <li>Two-stage heteroscedastic GPR with residual variance modeling</li> <li>Standard GPR baseline for comparison</li> <li>Synthetic data generation with controlled heteroscedastic noise</li> <li>Stratified spatial train-test split</li> <li>Comprehensive evaluation metrics (RMSE, R², coverage probability, interval width)</li> </ul> <h3>Repository Structure</h3> <ul> <li><code>main_balanced.py</code> - Main working script</li> <li><code>data/</code> - Data generation and preprocessing</li> <li><code>models/</code> - GPR and HGPR implementations</li> <li><code>evaluation/</code> - Metrics and cross-validation</li> <li><code>visualization/</code> - Plotting utilities</li> </ul> <h3>Results Files</h3> <ul> <li><code>hgpr_results_balanced.csv</code> - Detailed predictions for 101 test samples</li> <li><code>hgpr_results_balanced.png</code> - Publication-ready visualization</li> <li><code>model_comparison_balanced.csv</code> - Summary comparison table</li> </ul> <h3>How to Run</h3> <pre><code class="language-bash">pip install -r requirements.txt python main_balanced.py </code></pre>