Landslide forecasting and factors influencing predictability
Forecasting a catastrophic collapse is a key element in landslide risk
reduction, but it is also a very difficult task owing to the scientific
difficulties in predicting a complex natural event and also to the severe
social repercussions caused by a false or missed alarm. A prediction is
always affected by a certain error; however, when this error can imply
evacuations or other severe consequences a high reliability in the forecast
is, at least, desirable.
In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability.