AUTHOR=Zeng Jin , Tuo Wanbing , Wang Xinchao , Zhao Xingchang 

TITLE=Landslide susceptibility assessment of upper Yellow River using coupling statistical approaches, machine learning algorithms and SBAS-InSAR technique

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1652646

DOI=10.3389/feart.2025.1652646

ISSN=2296-6463

ABSTRACT=Landslide disasters frequently occur in the upper reaches of the Yellow River, particularly within the Gonghe to Xunhua section. A precise evaluation of landslide susceptibility is vital for effective disaster prevention and mitigation. Integrated models that combine statistical methods with machine learning techniques have been widely adopted for landslide susceptibility assessments. However, the quality and composition of the positive sample training data have a significant impact on the accuracy of the outcomes. This study uses historical landslide data from the region and applies two statistical approaches-the information value (IV) and the coefficient of determination (CF) methods-alongside three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Six integrated models (IV-RF, IV-SVM, IV-XGBboost, CF-RF, CF-SVM, and CF-XGBoost) are developed to evaluate landslide susceptibility in the Yellow River’s upper reaches (from Gonghe to Xunhua). The Receiver Operating Characteristic (ROC) curve and Accuracy (ACC) values are used to assess the models’ performance, while spatial features of newly identified landslides, determined through optical remote sensing images, are compared using Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. The CF-XGBoost model is identified as the most effective. New landslide data were then added to the positive sample dataset to retrain the CF-XGBoost model, enhancing its predictive performance. The methodology proposed in this study not only enables effective evaluation of the accuracy and reliability of computational results derived from ensemble models, but also addresses the limitations caused by untimely acquisition of insufficient landslide samples. Furthermore, the resulting landslide susceptibility assessment establishes a reliable technical foundation for local disaster management authorities to formulate scientifically sound risk mitigation and control strategies.