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Beyond accuracy: Stabilizing feature importance in GWRF/RF for soil heavy metal mapping
1Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo, 135-8181, Japan.
Qin et al. (2025) mapped soil heavy metals using geographically weighted random forest (GWRF) and random forest (RF), improving accuracy. They identified key predictors but noted limitations in feature importance validation, recommending rank-stability auditing for robust interpretation.
Area of Science:
- Environmental Science
- Geospatial Analysis
- Soil Science
Background:
- Soil heavy metal contamination poses risks to ecosystems and human health.
- Accurate mapping of soil heavy metals is crucial for environmental management.
- Geospatial modeling techniques are increasingly used for soil property prediction.
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