aceHybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion
Riverbank erosion is a catastrophic geomorphological hazard that poses severe ecological and socio-economic challenges across densely populated floodplains. This study advances a machine learning (ML) framework that integrates individual and bagging-classifier approaches using random forest (RF), multilayer perceptron (MLP) and bagging classifiers to assess landscape ecological vulnerability (LEV) to riverbank erosion. The site-specific environmental, climatic, geomorphological and ecological parameters were sel…
A bagging ensemble combining random forest and multilayer perceptron improved landscape ecological vulnerability mapping for riverbank erosion, reaching 0.97 AUC and enabling targeted land management for disaster risk reduction.
Evidence
- Peer-reviewedEnvironmental Monitoring and Assessment2026-08-15
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Truvace Impact Record TRV-2026-0807, v1: “Hybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion.” Truvace, 2026-08-17. /record/TRV-2026-0807 (accessed at citation time). sha256 b979c24aa6f668b0…
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