Abstract
To evaluate the performance of deep learning methods on the landslide susceptibility mapping, five different convolutional neural networks (CNN)—AlexNet, Inception-v3, Xception, ResNet-101, and DenseNet-201—were employed to predict the landslide susceptibility along a transmission line. Ten landslide influencing factors were extracted from three databases and considered in the input layers. The landslide (10,481 grids) and non-landslide (10,481 grids) data were randomly subdivided into 70% (14,673 grids) and 30% (6289 grids) to construct the training and validation samples, respectively. The appropriate architecture and training parameters were carefully selected after many attempts until the training and validation accuracy was above 90%. The receiver operating characteristic (ROC) curve, landslide density (LD), and landslide ratio (LR) were determined to estimate the five CNN networks’ prediction accuracy. All CNN networks had high area-under-the-curve (AUC) values when assessing landslide susceptibility, and most landslides occurred in the outputs with predicted high and very high landslide susceptibility (LD > 65% and LR > 2.90). Generally, CNN networks had a higher accuracy than the two traditional methods due to the powerful capability of deep feature extraction. Additionally, the computational time cost in three steps was recorded to investigate the efficiency of five CNN networks, and all CNN networks accomplished the goals within an acceptable time using a commercially available computer (~ 24 h). Comparatively, AlexNet and Xception had better performance than other networks on the landslide susceptibility assessment.
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: The data and materials that support the findings of this study are available from the first and corresponding authors upon reasonable request.
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Appendix
Appendix
Definitions and terminology
Soil landslide: A type of landslide occurring when loose dirt or sediment falls down a slope along a failure plane.
Rock landslide: A type of landslide occurring when a mass of rock move quickly downslope along a failure plane.
Aspect: The dip direction that the downhill slope faces.
Slope: The dip angle of a slope, representing the steepness of the terrain surface.
Topographic-bedding-intersection-angel index (TOBIA): A measure to assess the role of slope geometry (external slope geometry and internal slope geometry) in landslides occurring.
Elevation: The height of a point above (or below) sea leave.
Topographic wetness index (TWI): A topographic index used to quantify the control of local topography on hydrological processes and to indicate the spatial distribution of soil moisture and surface saturation.
Stream power index (SPI): A measure used to describe potential flow erosion on the topographic surface.
Lithostratigraphy: The classification of bodies of rock according to lithologic properties of the strata and their relative stratigraphic relations.
Distance to fault: The Euclidean distance from a given point to the center of a fault.
Distance to road: The Euclidean distance from a given point to the center of a road.
Normalized difference vegetation index (NDVI): A remote sensing technique used to quantify vegetation health and density by measuring the difference between a vegetation’s visible red and near-infrared (NIR) reflectances.
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Ge, Y., Liu, G., Tang, H. et al. Comparative analysis of five convolutional neural networks for landslide susceptibility assessment. Bull Eng Geol Environ 82, 377 (2023). https://doi.org/10.1007/s10064-023-03408-9
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DOI: https://doi.org/10.1007/s10064-023-03408-9