Integrating Multicriteria Environmental Factors and Spatially Cross-Validated Machine Learning for Synthetic Erosion Vulnerability Assessment in the Betsiboka Region, Madagascar
Domaine:
environment and energygeospatialType de record:
paper
Créateur:
RasRakTovRaz
Éditeur:
Int
Hôte:
This study integrates multicriteria environmental analysis and spatially cross-validated machine learning to assess
synthetic erosion vulnerability in the Betsiboka Region, Madagascar. The vulnerability index combined low NDVI, slope,
hydrographic proximity, elevation, and available geological, soil, rainfall, and land-cover factors on a 1 km grid.
Hydrographic proximity was modelled using an exponential distance-decay function with a characteristic distance of 3 km,
while geology was represented through normalized susceptibility scores ranging from 0 to 1. Three regression algorithms—
Random Forest, Extra Trees, and Histogram Gradient Boosting—were evaluated using five-fold spatial block crossvalidation.
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