Influence of river incision on landslides triggered in Nepal by the Gorkha earthquake: Results from a pixel-based susceptibility model using inlabru

2025-07-11

AI Summary

This study addresses the neglect of fluvial erosion in landslide susceptibility modeling following the Gorkha earthquake. We propose a Bayesian spatial point process framework integrating river dynamics, innovatively incorporating the normalized channel steepness index ($k_{sn}$) as a physically grounded covariate representing the coupling between river incision and hillslope erosion. To resolve spatial misalignment between landslide point data and raster covariates, we employ the *inlabru* framework. The model jointly predicts landslide locations and size distributions while quantifying predictive uncertainty. Cross-validation reveals that higher $k_{sn}$ values significantly increase landslide occurrence probability but do not influence landslide size. The resulting susceptibility maps exhibit strong transferability and physical interpretability. This work establishes a scalable, physics-informed paradigm for seismic landslide hazard assessment in complex terrain.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This study presents a comprehensive framework for modelling earthquake-induced landslides (EQILs) through a channel-based analysis of landslide centroid distributions. A key innovation is the incorporation of the normalised channel steepness index ($k_{sn}$) as a physically meaningful and novel covariate, inferring hillslope erosion and fluvial incision processes. Used within spatial point process models, $k_{sn}$ supports the generation of landslide susceptibility maps with quantified uncertainty. To address spatial data misalignment between covariates and landslide observations, we leverage the inlabru framework, which enables coherent integration through mesh-based disaggregation, thereby overcoming challenges associated with spatially misaligned data integration. Our modelling strategy explicitly prioritises prospective transferability to unseen geographical regions, provided that explanatory variable data are available. By modelling both landslide locations and sizes, we find that elevated $k_{sn}$ is strongly associated with increased landslide susceptibility but not with landslide magnitude. The best-fitting Bayesian model, validated through cross-validation, offers a scalable and interpretable solution for predicting earthquake-induced landslides in complex terrain.

Problem

• Model earthquake-induced landslides using channel-based analysis
• Incorporate normalized steepness index for landslide susceptibility
• Overcome spatial misalignment in data for accurate predictions

Innovation

• Uses normalized channel steepness index ($k_{sn}$)
• Leverages inlabru for spatial data alignment
• Bayesian model for scalable landslide prediction