Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district
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View abstract on PubMed
Summary
This summary is machine-generated.Boosted Regression Trees (BRT) effectively mapped landslide susceptibility in Bangladesh, outperforming Random Forest and K-Nearest Neighbor. Maximum rainfall and elevation were key factors, aiding disaster management.
Area Of Science
- Geosciences and Environmental Science
- Machine Learning Applications in Natural Hazard Assessment
Background
- Landslides represent a significant threat to human populations, infrastructure, and buildings, particularly in rugged terrains.
- The Chattogram Hill Tract region in Bangladesh is prone to frequent landslides, especially during monsoon seasons, necessitating effective risk assessment.
- Accurate landslide susceptibility (LS) mapping is crucial for developing mitigation and disaster management strategies.
Purpose Of The Study
- To conduct a comparative analysis of innovative machine learning (ML) algorithms for landslide susceptibility mapping in Bangladesh's Khagrachari district.
- To identify the most effective ML model for predicting landslide-prone areas.
- To determine the most influential landslide conditioning factors in the study region.
Main Methods
- Utilized a dataset comprising 15 landslide conditioning factors and 127 landslide inventory points (71 landslide, 56 non-landslide).
- Employed three ML algorithms: Random Forest (RF), Boosted Regression Trees (BRT), and K-Nearest Neighbor (KNN) for LS mapping.
- Validated model performance using Area Under the Curve (AUC), overall accuracy, precision, and recall metrics.
Main Results
- The Boosted Regression Trees (BRT) model exhibited the highest performance, achieving an AUC of 0.95, superior to RF (0.91) and KNN (0.86).
- BRT also demonstrated superior overall accuracy (0.82), precision (0.81), and recall (0.86), confirming its effectiveness.
- Maximum rainfall and elevation were identified as the most significant factors influencing landslide occurrence for both BRT and RF models.
Conclusions
- Boosted Regression Trees (BRT) is the most effective machine learning algorithm for landslide susceptibility mapping in the Khagrachari district.
- The findings provide critical insights for understanding landslide risks and informing decision-making for disaster management and mitigation.
- The methodology and results can be scaled for broader application in natural hazard response planning across similar regions.
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