Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard ass...Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment.Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model,which characterizes the slope stability in terms of a factor of safety(FS) value.Different approaches have been followed to also assess and propagate uncertainty through such models.Haneberg(2004) introduced the use of the First Order Second Moment(FOSM) method to propagate input uncertainty through the infinite slope model,further developing the model and implementing it in the PISA-m software package(Haneberg,2007).Here we present an ArcPy implementation of PISA-m algorithms,which can be run from ESRI ArcMap in an entirely consistent georeferenced framework,and which we call "GIS Tool for Infinite Slope Stability Analysis"(GIS-TISSA).Users can select between different input options,e.g.,following a similar input style as for PISA-m,i.e., using an ASCII.csv parameters input file,or providing each input parameter as a raster or constant value,through the program graphic user interface.Analysis outputs can include FS mean and standard deviation estimates,the probability of failure(FS <1), and a reliability index(RI) calculation for FS.Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done,for which raster files of the mean,standard deviation,probability of exceedance,and RI are also generated.Verification of the code is done by replicating the results obtained with the PISA-m code for different input options,within a 10-5 relative error.Monte Carlo modeling is also applied to validate GIS-TISSA outputs,showing a good overall correspondence.A case study was performed for Kannur district,Kerala,India,where an extensive landslide databa se for the year 2018 was available.81.19% of the actual landslides fell in zones identified by the model as unstable.GIS-TISSA provides a user-friendly interface,particularly for those users familiar with ESRI ArcMap,that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions.The ArcPy toolbox is provided as a.pyt file as an appendix as well as hosted at the weblink:https://pages.mtu.edu/~toommen/GeoHazard.html.展开更多
文摘Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment.Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model,which characterizes the slope stability in terms of a factor of safety(FS) value.Different approaches have been followed to also assess and propagate uncertainty through such models.Haneberg(2004) introduced the use of the First Order Second Moment(FOSM) method to propagate input uncertainty through the infinite slope model,further developing the model and implementing it in the PISA-m software package(Haneberg,2007).Here we present an ArcPy implementation of PISA-m algorithms,which can be run from ESRI ArcMap in an entirely consistent georeferenced framework,and which we call "GIS Tool for Infinite Slope Stability Analysis"(GIS-TISSA).Users can select between different input options,e.g.,following a similar input style as for PISA-m,i.e., using an ASCII.csv parameters input file,or providing each input parameter as a raster or constant value,through the program graphic user interface.Analysis outputs can include FS mean and standard deviation estimates,the probability of failure(FS <1), and a reliability index(RI) calculation for FS.Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done,for which raster files of the mean,standard deviation,probability of exceedance,and RI are also generated.Verification of the code is done by replicating the results obtained with the PISA-m code for different input options,within a 10-5 relative error.Monte Carlo modeling is also applied to validate GIS-TISSA outputs,showing a good overall correspondence.A case study was performed for Kannur district,Kerala,India,where an extensive landslide databa se for the year 2018 was available.81.19% of the actual landslides fell in zones identified by the model as unstable.GIS-TISSA provides a user-friendly interface,particularly for those users familiar with ESRI ArcMap,that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions.The ArcPy toolbox is provided as a.pyt file as an appendix as well as hosted at the weblink:https://pages.mtu.edu/~toommen/GeoHazard.html.
