Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques

Abstract

Landslide susceptibility assessment (LSA) evaluates the likelihood of landslide occurrences and can help mitigate and prevent landslide risks. Recently, there have been vast applications of data-driven LSA methods owing to the increased availability of high-quality satellite data and landslide inventories. However, two issues remain to be addressed, as follows: (a) Items in a landslide inventory are mainly historical landslides from the interpretation of optical images and site investigation, resulting in predictive models trained with these items being insensitive to undetectable slope movements, such as slow-moving landslides that have not yet occurred; (b) Most study areas contain a variety of landslide-prone geographical settings that a single model can not accommodate well. Considering the complex landslide causes in Hong Kong with a land area of approximately 1108 km2, we proposed the utilization of multi-temporal InSAR techniques to generate weak landslide samples from slopes with ground surface movements for landslide inventory augmentation; and meta-learn intermediate representations for the fast adaptation of LSA models corresponding to different landslide-prone geographical settings. Besides, we performed feature permutation to identify dominant landslide-predisposing factors. The LSA results in Hong Kong revealed that slope deformation in several mountainous areas is closely associated with the occurrence of recorded landslides. By augmenting the landslide inventory using InSAR techniques, the proposed method enhanced the LSA models' capacity to identify slow-moving landslides and achieved better statistical performance. The discussion highlights that slope and stream power index (SPI) are the key landslide-predisposing factors in Hong Kong, but the dominant landslide-predisposing factors will vary under different geographical conditions. By comparison with the methods that treat LSA as a binary classification problem, such as support vector machine, multilayer perceptron, deep belief network, and random forest based LSA methods, the proposed method entails a fast-learning strategy and outperforms these methods in data-driven model evaluation indicators, e.g., by 3–6% in accuracy, 2–6% in precision, 1–2% in recall, 3–5% in F1-score, and approximately 10% in Cohen Kappa. The information about the relative importance of landslide predisposing factors, derived through feature permutation, can foster guidance for targeted landslide prevention schemes, such as constructing and maintaining slope consolidation facilities in areas where slope is the dominant landslide-predisposing factor.