Introducing New Framework to Capture Extreme Uncertainty in Risk Management of Landslides

Landslide is a complicated multi-hazard event that can turn into a great disaster; burying part of a city or village downhill the slope and causing a large number of fatalities. Landslide risk assessment and management (LRAM) process involves decision making to find an optimal loss-mitigation approach. A good, representative decision analysis process relies on reasonably capturing all possible scenarios that would happen in a landslide event and assessing the probabilities. This requirement for the evaluation of possible scenarios exists while there are multiple sources of uncertainties in such a problem and our knowledge of extreme events is limited. In our research, we showed that using the historical data and extending past knowledge to predict a future event is not completely applicable and reliable. Thus, it is important to consider the possibility of irrelevancy of historical data to the problem. The research shows that to consider extreme and rare events, it is required to use a non-informative prior probability in the Bayesian analysis. Such non-informative prior probability distribution is obtained by maximizing the entropy of possible events, which maximizes the lack of information. This paper includes a summary of our research to develop a new decision-based framework in order to reasonably capture sources of uncertainties in the landslide risk management decision analysis. In addition, a practical example of application of the framework to a landslide case study will be presented.