ML-Powered Landslide and Geological Hazard Prediction: Integrating Spatial Autocorrelation for Improved Susceptibility Mapping
Keywords:
landslide susceptibility mapping, spatial autocorrelation, Moran’s I, machine learning, spatial cross-validation, geospatial modeling, risk mappingAbstract
Landslides and structural geological hazards offer increased loss of life, infrastructure, and such other ecosystem services, mainly in the mountainous landscape and also in rapidly urbanizing scenarios. Learned machine (ML) methods have emerged as a strong approach to landslide susceptibility mapping (LSM). However, many ML pipelines consider the hydro-geological observations as i.i.d., hence ignoring spatial autocorrelation and spatial sampling bias. Here, we put forth a reproducible conceptual framework that builds spatial dependence measures-global Moran's I, local indicator of spatial association measures, and spatial blocking-cross-validation directly into feature engineering, model fitting, and model validation. A synthetic but geophysically plausible example allows us to compare a non-spatial baseline, called Random Forest, to the spatially informed version that respects neighborhood structure during training and evaluation. We show that putting spatial blocks to use brings about less optimistic bias in accuracy, while local autocorrelation detects hotspots and coldspots of misclassification. Distance-aware features and geographically weighted sampling can enhance AUC and spatial generalization. Finally, we provide guidelines for implementation: transparent data provenance, interpretable model diagnostics, and risk-aware communication for practitioners in the government, infrastructure, and insurance sectors. Although the results are only illustrative, the end-to-end workflow can be directly leveraged in real regions with DEMs, land-cover products, lithological maps, and landslide inventories.
DOI: https://doi.org/10.24321/2456.9925.202505
References
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Goetz, J. N., Guthrie, R. H., & Brenning, A. (2011). Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129(3–4), 376–386.
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