aceHybrid ensemble machine learning algorithms for landscape ecological vulnerability assessment to riverbank erosion
Abstract: 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…
Rocky stream bed and eroded riverbank by Suglo20. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0
On August 15, 2026, a peer-reviewed study reported a hybrid ensemble machine learning framework for assessing landscape ecological vulnerability to riverbank erosion. Using random forest, multilayer perceptron and bagging classifiers with multicollinearity-selected environmental and geomorphological parameters, the bagging ensemble achieved 0.97 AUC and 0.91 accuracy, mapping over half the area into high or very high vulnerability zones in Bihar and West Bengal.
The result matters because it provides a scalable, high-performing tool for targeting land management and disaster risk reduction in densely populated floodplains where erosion threatens ecology and livelihoods. What remains uncertain is how the model transfers beyond the studied reaches, how interventions based on the maps perform over time, and whether additional socio-economic variables would change vulnerability patterns.
- Study integrated individual and bagging-classifier approaches using random forest, multilayer perceptron and bagging classifiers to assess landscape ecological vulnerability to riverbank erosion.
- Site-specific environmental, climatic, geomorphological and ecological parameters were selected by employing a multicollinearity test and validated with accuracy, precision, recall, F1-score and AUC.
- Very high-vulnerability zone covered 27.6% of area followed by high 25.8%, concentrated in middle and lower reaches particularly in Bihar and West Bengal.
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.
The rundown
Researchers built a hybrid ensemble framework that combined random forest and multilayer perceptron with bagging classifiers, selecting environmental, climatic, geomorphological and ecological predictors after multicollinearity testing. Performance was evaluated using accuracy, precision, recall, F1-score and AUC.
The ensemble achieved the best results with 0.91 accuracy and 0.97 AUC, classifying 27.6% of the study area as very high vulnerability and 25.8% as high, concentrated in middle and lower reaches in Bihar and West Bengal. Sensitivity analysis identified elevation, rainfall, soil type and geomorphology as most influential, with removal of elevation causing the largest performance drop.
Sources
- Peer-reviewedEnvironmental Monitoring and Assessment2026-08-15
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