Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Dyer, Alec S.; Mark-Moser, MacKenzie; Duran, Rodrigo; Bauer, Jennifer R.

doi:10.1007/s11069-024-06492-6

Title: Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Journal Article · Sat Feb 24 00:00:00 EST 2024 · Natural Hazards

DOI:https://doi.org/10.1007/s11069-024-06492-6· OSTI ID:2311905

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Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FWP 1025020

OSTI ID:: 2311905

Journal Information:: Natural Hazards, Journal Name: Natural Hazards; ISSN 0921-030X

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Netherlands

Language:: English

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