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Coseismic landslide hazard modeling methodologies - 3. Discussion and Conclusions

 

3. Discussion and Conclusions     

There are a variety of landslide hazard modeling methods. Methods may be optimally calibrated based on certain regions, certain seismic events, and associated predictor variables.

Logistic regressions are optimal for modeling spatial and temporal predictions of landslide events, resulting in low conditional error rates. However, logistic regressions are limited by their linear nature of covariant relationships, meaning, fewer variables may be accounted for in the model. Conversely, this allows for increased generalization.

The non-linear nature of an artificial neural network accounts for more complex relationships between variables and predicts classification schemes with increased accuracy. However, it is important to consider that increased variable incorporation limits the generalization capability of the model and increases the time necessary to train the model. Moreover, the network produces relationships between the variables that are most likely to result from the supplied training data, but model is not guaranteed to find the most ideal solutions and may not reach an absolute minimum error.

Physical models of landslide susceptibility are ideal to use in areas of incomplete landslide inventories. The model can take into account physical processes of an area prior to landslide events and predict the spatial distribution of displacement in a specified shaking scenario. Mechanical laws are used to understand the effects of seismic activity and its relation to slope stability.

Landslide hazard analyses aim to find the optimal model that produces no false-negatives and no false-positives spatially and temporally on a global scale. There is no current universal landslide hazard model, as susceptibility may be affected by certain geomorphic or climatic variables particular to a specific area, limiting assessment to a regional scale. Further, spatial and temporal models are limited as spatial models lack incorporation of environmental conditions which induce rock-strength weakening and temporal models do not incorporate pre-event variables, such as rainfall prior to a seismic event, or tectonic and volcanic activity.  Further improvement to hazard analysis is necessary in order to more correctly predict landslide hazard in space and time. Multiple methods are currently being developed in order to accommodate these shortcomings. 

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