Reliable assessment approach of landslide susceptibility in broad areas based on optimal slope units and negative samples involving priori knowledge. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Fu, Xiao; Liu, Yuefan; Zhu, Qing; Ge, Daqing; Li, Yun; Zeng, Haowei
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 2495
• Page End: 2510
• Publication Date: 2022-12-31
• Subjects: Slope units -- mapping units -- landslide susceptibility assessment -- digital elevation model -- certainty factor -- machine learning; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2159549
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90.