A Novel Method using Explainable Artificial Intelligence (XAI)-based Shapley Additive Explanations for Spatial Landslide Prediction using Time-Series SAR dataset

TL;DR: This systematic literature review explores Explainable AI (XAI) applications in disaster risk management, analyzing 68 publications to identify achievements, challenges, and limitations, and providing insights to enhance XAI's effectiveness in disaster decision-making and strategy planning.

TL;DR: In this article , the authors compared the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF).

TL;DR: This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develops measures to mitigate the risk in the Province of Sondrio, Italy to provide a valuable tool for creating new strategies to reduce the harm and damage caused by slow avalanches in the region.

TL;DR: It is suggested that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time and the number of user‐defined parameters required byrandom forest classifiers is less than the number required for SVMs and easier to define.

TL;DR: In this article, the authors outline the principles for landslide mapping, and review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories.

TL;DR: In this paper, a critical review of statistical methods for landslide susceptibility modelling and associated terrain zonations is presented, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria.

Abstract: . Landslides are a ubiquitous hazard in terrestrial environments with slopes, incurring human fatalities in urban settlements, along transport corridors and at sites of rural industry. Assessment of landslide risk requires high-quality landslide databases. Recently, global landslide databases have shown the extent to which landslides impact on society and identified areas most at risk. Previous global analysis has focused on rainfall-triggered landslides over short ∼ 5-year observation periods. This paper presents spatiotemporal analysis of a global dataset of fatal non-seismic landslides, covering the period from January 2004 to December 2016. The data show that in total 55 997 people were killed in 4862 distinct landslide events. The spatial distribution of landslides is heterogeneous, with Asia representing the dominant geographical area. There are high levels of interannual variation in the occurrence of landslides. Although more active years coincide with recognised patterns of regional rainfall driven by climate anomalies, climate modes (such as El Nino–Southern Oscillation) cannot yet be related to landsliding, requiring a landslide dataset of 30 + years. Our analysis demonstrates that landslide occurrence triggered by human activity is increasing, in particular in relation to construction, illegal mining and hill cutting. This supports notions that human disturbance may be more detrimental to future landslide incidence than climate.