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    Space-time landslide hazard modeling via Ensemble Neural Networks

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    Name:
    dahalal2022_reduced_removed.pdf
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    1.469Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Dahal, Ashok cc
    Tanyas, Hakan cc
    van Westen, C.J. cc
    van der Meijde, Mark
    Mai, Paul Martin cc
    Huser, Raphaël cc
    Lombardo, Luigi cc
    KAUST Department
    Computational Earthquake Seismology (CES) Research Group
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Statistics Program
    KAUST Grant Number
    URF/1/4338-01-01
    Date
    2022-06-02
    Permanent link to this record
    http://hdl.handle.net/10754/678608
    
    Metadata
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    Abstract
    For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published research have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size, has hardly ever been modeled over space and time. However, the technological advancements of data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size) mentioned above. This work, takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this ambitious task, we have used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake on the 25th of April 2015. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 x 1 km and classified/regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6-month resolution depending on the mapped inventory dates. The results are promising as our model performs satisfactorily both in the classification (susceptibility) and regression (density prediction) tasks. We believe that the model we propose brings a level of novelty that has the potential to create a rift with respect to the common susceptibility literature, finally proposing an integrated framework for hazard modeling in a data-driven context.
    Citation
    Dahal, A., Tanyas, H., van Westen, C., van der Meijde, M., Mai, P. M., Huser, R., & Lombardo, L. (2022). WITHDRAWN: Space-time landslide hazard modeling via Ensemble Neural Networks. https://doi.org/10.31223/x5b075
    Sponsors
    This article was supported by King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, Grant URF/1/4338-01-01.
    Publisher
    California Digital Library (CDL)
    DOI
    10.31223/x5b075
    Additional Links
    http://eartharxiv.org/repository/view/3382/
    ae974a485f413a2113503eed53cd6c53
    10.31223/x5b075
    Scopus Count
    Collections
    Preprints; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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