Keywords
Landslide modelling; Object Modeling System; Models calibration
Location
Session C1: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models
Start Date
16-6-2014 3:40 PM
End Date
16-6-2014 5:20 PM
Abstract
Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Predicting shallow landslide susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, statistics. Usually to accomplish this task two main approaches are used: a statistical model or a physically based model.
In this paper, three physically based models for landslide susceptibility analysis were integrated in the Object Modelling System (OMS) and tested in real world applications. Models presented have an increasing degree of complexity. Effects of three different hydrological components connected to the stability model and of model parameters optimization were investigated.
Their integration in OMS allows the use of other components such as GIS tools to manage input-output processes, and automatic calibration algorithms to estimate model parameters. Finally, model performances were quantified by using traditional goodness of fit indices such as accuracy, bias score, hit rate, and success index. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk Monitoring, Early Warning and Mitigation Along the Main Lifelines," CUP B31H11000370005, in the framework of the National Operational Program for "Research and Competitiveness" 2007-2013.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Physically based landslide susceptibility models with different degree of complexity: calibration and verification
Session C1: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models
Rainfall induced shallow landslides cause significant damages involving loss of life and properties. Predicting shallow landslide susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, statistics. Usually to accomplish this task two main approaches are used: a statistical model or a physically based model.
In this paper, three physically based models for landslide susceptibility analysis were integrated in the Object Modelling System (OMS) and tested in real world applications. Models presented have an increasing degree of complexity. Effects of three different hydrological components connected to the stability model and of model parameters optimization were investigated.
Their integration in OMS allows the use of other components such as GIS tools to manage input-output processes, and automatic calibration algorithms to estimate model parameters. Finally, model performances were quantified by using traditional goodness of fit indices such as accuracy, bias score, hit rate, and success index. This research was funded by PON Project No. 01_01503 "Integrated Systems for Hydrogeological Risk Monitoring, Early Warning and Mitigation Along the Main Lifelines," CUP B31H11000370005, in the framework of the National Operational Program for "Research and Competitiveness" 2007-2013.
