Articles | Volume 16, issue 12
Nat. Hazards Earth Syst. Sci., 16, 2501–2510, 2016
https://doi.org/10.5194/nhess-16-2501-2016
Nat. Hazards Earth Syst. Sci., 16, 2501–2510, 2016
https://doi.org/10.5194/nhess-16-2501-2016

Research article 30 Nov 2016

Research article | 30 Nov 2016

Landslide forecasting and factors influencing predictability

Emanuele Intrieri and Giovanni Gigli Emanuele Intrieri and Giovanni Gigli
  • Department of Earth Sciences, University of Studies of Firenze, via La Pira 4, 50121 Florence, Italy

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.

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.

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Short summary
Forecasting a landslide collapse is a key element in risk reduction, but it is also a very difficult task due to scientific difficulties in predicting a complex natural event and the severe social repercussions caused by a false or missed alarm. In order to help decision makers, we propose a method of increasing the confidence when making landslide predictions. This study also helps understand how geology affects landslide predictability by introducing a predictability index.
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