Comparative Analysis
of Machine Learning Models and Hybrid Ensemble Approach’s for Landslide Prediction
Fayaz Mohsin and Kanti Tushar
Disaster Advances; Vol. 17(11); 26-34;
doi: https://doi.org/10.25303/1711da026034; (2024)
Abstract
The NH-44 Jammu Srinagar National Highway in India is susceptible to landslides,
rock falls and shooting stones due to its geological characteristics and steep slopes.
This study aims to compare the performance of various Machine Learning (ML) algorithms
and hybrid models in predicting landslides using historical data. Seven optimized
ML approaches Support Vector Classifier (SVC), Logistic Regression (LR), Decision
Tree (DT), K Nearest Classifier (KNC), Random Forest (RF), GaussianNB (GNB) and
AdaBoost Classifier (ABC) are used.
Additionally, two Hybrid Ensemble methods, the Voting Hybrid Model (VHM) and Stacking
Hybrid Model (SHM), are introduced. The performance of each model is evaluated using
accuracy, precision, recall, F1-score and AUC metrics. Results indicate that all
the models performed well, with hybrid ensemble models surpassing all individual
algorithms. The Stacking Hybrid Model (SHM) excels achieving 99.4% accuracy and
98.5% AUC, outperforming the Voting Hybrid Model (VHM). Hybrid models consistently
outperform individual models in accuracy and AUC. These proposed methods exhibit
robustness and enhanced results in addressing this issue. This framework can help
to predict the landslides with high accuracy which can save the lives through timely
evacuations from high-risk areas.
