Department of Earth Sciences, University of Studies of Firenze, via
La Pira 4, 50121 Florence, Italy
Received: 20 Jun 2016 – Discussion started: 28 Jun 2016
Abstract. 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.
Revised: 22 Sep 2016 – Accepted: 31 Oct 2016 – Published: 30 Nov 2016
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.
Intrieri, E. and Gigli, G.: Landslide forecasting and factors influencing predictability, Nat. Hazards Earth Syst. Sci., 16, 2501-2510, doi:10.5194/nhess-16-2501-2016, 2016.