Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Abstract

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

Automatic identification of streamlined subglacial bedforms using machine learning: an open-source Python approach

“…In particular, Random Forest (Breiman 2001), is a powerful ensemble algorithm that is well-suited for enhancing generalizability, and XGBoost (Chen and Guestrin 2016) is another ensemble algorithm that is known for improving prediction accuracy on lesser understood data at scale. While this study is a new application of these learning methods in the field of glacial geomorphology, Random Forest and XGBoost algorithms have been recently employed for other automated mapping applications in geology (e.g., Zhao and Chen 2023, Li et al 2022, Ali et al 2024 where they have both demonstrated high predictive performance.…”