Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to p...

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Bibliographic Details
Main Authors: Zeynab Yousefi, Ali Asghar Alesheikh, Ali Jafari, Sara Torktatari, Mohammad Sharif
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Information
Subjects:
landslide susceptibility mapping
stacking ensemble technique
machine learning
Boruta–XGBoost
feature selection
meta-heuristic algorithms
Online Access:https://www.mdpi.com/2078-2489/15/11/689
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