A tool to compute the landslide degree of risk using R-Studio and R-Shiny

Landslide risk zones are common in urban areas of Brazil. Around the world, studies to map landslide hazard are common and they use different methodologies; however, very few studies focus on mapping landslide risk areas. In occupied urban areas, mapping the landslide hazard doesn't solve the problem since now it is not a matter of being able or not to build in hazard zone but knowing where the risk areas are so the municipality can manage these areas closely. The City Ministry/IPT Methodology has been used to map the landslide degree of risk in many municipalities. This method requires fieldwork and a decision as to the degree of risk based on the personal judgment of technicians in the field and also require time and money. To make the degree of risk less dependent on the judgment of technicians in the field and allow municipalities to update their risk map, we have developed an app using RStudio software and the Shiny app package. The app computes the degree of risk automatically using data inputs and each parameter's weight. Consequently, the app makes it easy to calculate the degree of landslide risk and decreases the subjectivity. Moreover, it is a tool that is free of costs as it uses open source software. Local governments will be better able to create and update maps of their landslide risk areas. More importantly, this app will contribute to the mitigation of the landslide risk.

[1]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[2]  Erminia Maricato Brasil, cidades : alternativas para a crise urbana , 2002 .

[3]  A. Witt Using a GIS (Geographic Information System) to Model Slope Instability and Debris Flow Hazards in the French Broad River Watershed, North Carolina , 2005 .

[4]  David M. Cruden,et al.  LANDSLIDE TYPES AND PROCESSES , 1958 .

[5]  C. Mahler,et al.  Analytical Model of Landslide Risk Using GIS , 2012 .

[6]  P. Reichenbach,et al.  Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model , 2016 .

[7]  F. Dourado,et al.  O Megadesastre da Região Serrana do Rio de Janeiro: as causas do evento, os mecanismos dos movimentos de massa e a distribuição espacial dos investimentos de reconstrução no pós-desastre , 2013 .

[8]  J. Choi,et al.  Landslide susceptibility analysis using weight of evidence , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[9]  Greg Scott,et al.  Community Risk in Cairns: A Multi-hazard Risk Assessment , 1999 .

[10]  Thomas L. Saaty,et al.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[11]  Aplicação do modelo Shalstab para mapeamento da suscetilidade a escorregamentos rasos em Caraguatatuba, Serra do Mar (SP) , 2015 .

[12]  D. Montgomery,et al.  A physically based model for the topographic control on shallow landsliding , 1994 .

[13]  Piotr Jankowski,et al.  Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard , 2003, J. Geogr. Syst..

[14]  E. Sujatha,et al.  Assessing landslide susceptibility using Bayesian probability-based weight of evidence model , 2014, Bulletin of Engineering Geology and the Environment.

[15]  Netra R. Regmi,et al.  Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA , 2010 .

[16]  R. Soeters,et al.  Landslide hazard and risk zonation—why is it still so difficult? , 2006 .

[17]  Rex L. Baum,et al.  Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy , 2006 .

[18]  William E. Dietrich,et al.  Validation of the Shallow Landslide Model, SHALSTAB, for Forest Management , 2013 .

[19]  N. A. S. Hamm,et al.  Variance-based sensitivity analysis of the probability of hydrologically induced slope instability , 2006, Comput. Geosci..

[20]  D. Alexander Vulnerability to landslides. , 2012 .

[21]  M. Anderson,et al.  Landslide hazard and risk , 2005 .

[22]  H. Omar,et al.  Application of a Physically based Model for Terrain Stability Mapping in in North of Iran , 2013 .

[23]  Iswar Das Das,et al.  Spatial statistical modelling for assessing landslide hazard and vulnerability , 2011 .

[24]  Jerry D. Davis,et al.  Physical and maximum entropy models applied to inventories of hillslope sediment sources , 2013, Journal of Soils and Sediments.

[25]  Saro Lee,et al.  Landslide susceptibility analysis and verification using the Bayesian probability model , 2002 .

[26]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[27]  F. Fiorillo,et al.  Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach, optimized for initial water table conditions , 2013, Bulletin of Engineering Geology and the Environment.

[28]  David Alexander,et al.  Principles of Emergency Planning and Management , 2002 .

[29]  D. Varnes,et al.  Landslide types and processes , 2004 .