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Data-driven landslide forecasting: Methods, data completeness, and real-time warning
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DOI：10.1016/j.enggeo.2023.107068.png)
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Abstract
En 中文
Various data-driven methods, including empirical, statistical, and machine learning methods, have been devel-oped to promptly forecast rain-induced landslides. Their abilities differ considerably in spatio-temporal landslide prediction and in handling datasets of varying qualities. A challenging issue that significantly hinders the ap-plications of data-driven methods is the data incompleteness in most landslide inventories, particularly the lack of accurate landslide time that is a vital link between each landslide and its triggering rainstorm. This study systematically compares the performances of three categories of data-driven methods for landslide prediction and proposes a novel machine learning model featured by probabilistic landslide modelling for spatio-temporal landslide prediction. The integrated machine learning model can be developed on a realistic landslide database, regardless of whether the landslide timing information is known or not. It not only promptly predicts the spatio-temporal evolution of landslides during a rainstorm but also reliably characterises the factual landslide risk, which provides a powerful real-time decision-making tool for landslide early warning and risk management. The model is validated against the landslide incidents in Hong Kong in the past 35 years both spatially and temporally, and outperforms other data-driven models in both prediction ability and accuracy.
Keywords:
Rain-induced landslides
Landslide forecasting
Landslide risk
Data-driven methods
Machine learning
Journal
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8.4
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6.6K
Citations:
3.8W
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