Modelling the Livelihood Vulnerability Index (LVI-IPCC) with Machine Learning in Agro-Ecological Region I of Southern Zambia
Bibliographic record
Abstract
This study employed seven machine-learning algorithms: Random Forest, XGBoost, LightGBM, Support Vector Machine (SVM-RBF), Elastic Net, Multilayer Perceptron (MLP), and hybrid PCA-enhanced models to predict the Livelihood Vulnerability Index (LVI-IPCC) of smallholder farmers in Southern Zambia’s Agro-Ecological Region I. Using grouped cross-validation to prevent spatial bias, the PCA-MLP and Random Forest models emerged as top performers, achieving R² values above 0.95 and RMSE below 0.05. These models effectively captured nonlinear socio-ecological interactions that influence vulnerability. Feature importance analyses identified education, income, water access, and drought exposure as key predictors. The integration of dimensionality reduction (PCA) improved model stability and interpretability. These findings demonstrate that hybrid machine-learning approaches outperform traditional LVI aggregation in predicting household vulnerability, providing scalable, data-driven insights for climate adaptation planning. The results highlight the potential of artificial intelligence to revolutionize vulnerability assessments and inform targeted resilience strategies in regions affected by climate change.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.012 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".