The International Information Center for Geotechnical Engineers

Coseismic landslide hazard modeling methodologies - 2.b Methods and Analysis: Artificial Neural Network


2.b GIS and Back-Propagation Artificial Neural Networks

                  Artificial Neural Networks (ANN), non-linear bonds between three or more layers of nodes and connections, are made based on weighting of inputs (Paradeshi et al., 2013). Inputs generally consist of spectral bands from remote sensing images, textural features, and other inputs obtained from a Digital Elevation Model (DEM), such as slope, aspect, and elevation (Lillesand et al., 2008). ANN can accommodate many inputs, but additional information adds hidden layers that allow for more complex problems, which reduces the generalization ability and increases training time (Lillesand et al., 2008). The non-linearity of ANN makes it an effective landslide hazard assessment for large amounts of samples, and is further improved when paired with GIS, but it is not guaranteed to find the most ideal solution, as it may not reach an absolute minimum error (Pardeshi et al., 2013).


2.b.I Application: Li et al., 2012

                  Li et al. (2012) utilized GIS and back-propagation ANN methodology in order to analyze landslide susceptibility induced by rainfall or seismic activity. Different predictive variables are considered in hazard assessments for rainfall and earthquake induced landslides in order to produce the optimal model of landslide hazard, observe differences between spatial patterns of landslide events, and create an ANN by weighting of multiple predictor variables.


2.b.II Methodologies: The following procedures follow protocol of Li et al., 2012.

                  In order to model landslide hazard induced by rainfall and earthquakes, which differ mechanically and dynamically, Li et al. (2012) assigned different predictor variables to each landslide hazard assessment. The predictive variables considered in the rain induced landslide hazard assessment are slope gradient, elevation, slope height, and distance to the stream. Variables considered in the seismically induced landslide assessment are slope gradient, elevation, slope height, distance to the stream, and distance to the fault. 

                  A DEM was first processed in ArcGIS in order to select and extract predictive variables within the area of interest. Then, by statistical analysis, the relationship between predictor variables and landslide frequency was evaluated. Once complete, the ANN was trained and weights of predictive variables were established. Training the model helps to calculate the best-fit model of predictive variable contribution to landslide hazard.

                  In order to train the ANN model, Li et al. (2012) used a back-propagation algorithm in a MatLab software package developed by Hines (1997), which weights predictive variables and develops a best-fit prediction model. Landslide susceptibility predictions are then classified as low, medium, or high for both rain and seismically induced landslides, generating hazard maps.


2.b.III Analysis

                  Results from susceptibility mapping of both rain and seismically induced landslide events show that there is not a conclusive relationship between landslide hazard and lithology or catchment area. Further, Li et al. (2007) determined that slope aspect and elevation contribute to landslide susceptibility, as failures occurred most often on north facing slopes below 1200m elevation. Indicating certain variables, such as water saturation and degree of weathering, more strongly influence landslide susceptibility in both rainfall and earthquake induced landslide events.

Add comment

NOTE: The symbol < is not allowed in comments. If you use it, the comment will not be published correctly.

Security code
*Please insert the above-shown characters in the field below.

The Corporate Sponsors: