Work

Prior-Informed Local Landslide Susceptibility Modelling Using National-Scale Landslide Information: A Case Study of the Parlung Tsangpo Alpine Gorge Basin

Year: 2026

Type: article

Abstract: Landslides pose widespread threats to mountainous communities and infrastructure worldwide, yet susceptibility mapping in alpine gorge basins is often constrained by sparse and incomplete local inventories. Whether national-scale landslide information can be transformed into reliable local knowledge remains unclear, particularly where strong topographic and environmental heterogeneity limits the direct transfer of broad-scale models. Here, we use the Parlung Tsangpo Basin on the southeastern Tibetan Plateau as a test case and compare four strategies for introducing national-scale information: local baseline modelling, direct national-model transfer, weighted source–target joint training, and prior-informed local modelling. The experiments use the same conditioning factors, data-processing workflow, spatially independent test set, and evaluation metrics, allowing the transfer strategies to be assessed under controlled conditions. The prior-informed strategy treats the susceptibility probability produced by the national model as a geoscientifically interpretable external prior, which is then relearned and recalibrated by local samples, and achieves the best overall performance. On the independent test set, it reaches an area under the receiver operating characteristic curve (AUC) of 0.901 and reduces the expected calibration error (ECE) to 0.055, outperforming the local baseline, direct transfer, and joint training strategies. Its susceptibility map shows an extensive low-susceptibility background with spatially concentrated high-susceptibility patches, thereby reducing broad-scale overprediction while preserving local landslide-prone zones. These results indicate that national-scale landslide information is more effective when converted into a locally recalibrated probability prior than when transferred directly, providing a practical pathway for susceptibility assessment in data-scarce mountainous basins.

Source: Remote Sensing

DOI: 10.3390/rs18132167

Authors: Yantong Guo, Zhongxiang Jiang, Yuxuan Lin, Ge Qu, Weile Li, Xiaoai Dai

Institutions: Chengdu University of Technology, State Key Laboratory of Geohazard Prevention and Geoenvironment Protection

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,258