Is there a space in landslide susceptibility modelling: A case study of Valtellina Valley, Northern Italy

• Main Authors: KHANT, Min Naing, ANN, Mei Yi Victoria Grace, KAM, Tin Seong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Landslide Susceptibility; Geographically Weighted Logistic Regression; Logistic Regression; Explanatory Modelling; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9350; https://ink.library.smu.edu.sg/context/sis_research/article/10350/viewcontent/978_3_031_64605_8_16.pdf
• Institution: Singapore Management University

Landslides pose significant and ever-threatening risks to human life and infrastructure worldwide. Landslide susceptibility modelling is an emerging field of research seeking to determine contributing factors of these events. Yet, previous studies rarely explored the spatial variation of different landslide factors. Hence, this study aims to demonstrate the potential contribution of spatial nonstationarity in landslide susceptibility modelling using Global Logistic Regression (GLR) and Geographically Weighted Logistic Regression (GWLR). The second objective of this study is to demonstrate the important role of data preparation, data sampling, variable sensing, and variable selections in landslide susceptibility modelling. Using Valtellina Valley in Northern Italy as the study area, our study shows that by incorporating spatial heterogeneity and modelling spatial relationships, the measures of Goodness-of-fit of GWLR outperform the traditional GLR. Furthermore, the model outputs of GWLR reveal statistically significant factors contributing to landslides and the spatial variation of these factors in the form of coefficient maps and a landslide susceptibility map.