Novel deep learning algorithm in soil erodibility factor predicting at a continental scale
Bibliographic record
Abstract
Soil erosion poses significant environmental and economic challenges, adversely affecting soil fertility and global agricultural productivity. We developed a novel model based on the Multi-Head Squeeze-and-Excitation Residual One-Dimensional Convolutional Neural Network (MH-SE-Res1DNet) to predict the soil erodibility factor ( K ) across Europe, representing the first application of this model for such a purpose worldwide. We conducted a comparative analysis using five benchmark machine learning algorithms, i.e., Random Forest (RF), Artificial Neural Network–Multilayer Perceptron (ANN-MLP), Support Vector Regression (SVR), Alternating Model Tree (AMT), and Pace Regression (PR), to assess the efficacy of our model. The results showed that the MH-SE-Res1DNet deep learning model had an outstanding ability for the K prediction. The model's lowest error (MAE=0.0025, RMSE=0.0031) and highest coefficient of determination (R 2 =0.943) were attained during the validation phase. Benchmark models demonstrated lower performance compared to the MH-SE-Res1DNet model, with R 2 values ranging from 0.880 to 0.912 and slightly higher errors across MAE and RMSE metrics. The sensitivity analysis of MH-SE-Res1DNet showed that its performance depends predominantly on key soil factors, particularly topsoil texture (M) and organic matter (OM) concentration. This model establishes a data-driven framework that significantly advances soil erodibility prediction by leveraging machine learning. It surpasses traditional methods and existing machine learning approaches in accuracy, efficiency, and scalability, setting a new benchmark for soil conservation planning and enabling adaptable, evidence-based land management strategies across Europe and worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".