Toggle Main Menu Toggle Search

Open Access padlockePrints

Engineered slope failure susceptibility modelling using high spatial resolution geospatial data

Lookup NU author(s): Stephen Obrike, Professor Stuart Barr, Dr Pauline Miller


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2021, Springer-Verlag GmbH Germany, part of Springer Nature.Given the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hotspots of potential landslide susceptibility can be recognised. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors was developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters, while Compact Airborne Spectrographic Imager (CASI) imagery and existing national scale digital map datasets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors was developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. The derived topographic and land use properties are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. The model highlighted the importance of slope, concave curvature and permeable soils with variable intercalations accounting for over 80% of slope instability and an overall predictive capability of 77.75% based on independent validation dataset.

Publication metadata

Author(s): Obrike SE, Barr SL, Miller PE, Anudu GK

Publication type: Article

Publication status: Published

Journal: Bulletin of Engineering Geology and the Environment

Year: 2021

Volume: 80

Pages: 7361–7384

Online publication date: 19/08/2021

Acceptance date: 09/08/2021

ISSN (print): 1435-9529

ISSN (electronic): 1435-9537

Publisher: Springer Science and Business Media Deutschland GmbH


DOI: 10.1007/s10064-021-02413-0


Altmetrics provided by Altmetric