A GIS tool for infinite slope stability analysis (GIS-TISSA)

Escobar-Wolf, R. P.; Sanders, Jonathon; Vishnu, C.L.; Oommen, Thomas; Sajinkumar, K.S.

doi:10.1016/j.gsf.2020.09.008

Cited by 36 publications

(12 citation statements)

References 31 publications

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“…A customized GIS tool within ArcGIS software using Python scripting, that could construct the minimum spanning tree of a connected, undirected, and weighted road network by considering all important network junction points as well as distance and time as the cost factors [18]. A GIS tool was created for infinite slope stability analysis using a PISA-m algorithm based on the infinite slope model and the Fist Order Second Moment (FOSM) method, to assess the potential of seismic triggers in slope instability [19]. Another GIS tool was generated with Python scripting to support scale-dependent analysis of spatial heterogeneity with a focus on landscape ecology with results showing that valuable information can be obtained from the spatial distribution of gaps in the input data at Journal of Geographic Information System different scales [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

Ekeanyanwu¹,

Bose²,

Beavers³

et al. 2022

JGIS

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As a hazard, flood is an extremely important indicator of how a city is resilient to waterborne diseases and epidemics. Over many decades, flood as a hazard has been a major factor in inducing displacement of marginalized section of the people. Austin city within Central Texas has been identified as one of the major hotspots for flooding in recent decades. Thus, the objectives of the paper are two folded: 1) Empirically, we analyzed and mapped out the susceptibility levels from the factors of physical environments to assess the risk of urban flooding (rainfall data, surface water bodies and topography); in Austin, Texas and 2) Methodologically, we created a re-useable ArcGIS scripting tool that can be used by researchers to automate the process of flood risk modelling with certain criteria. The paper showcases a novel time sensitive building of a tool which will enable better visibility of flood within the city of Austin.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

Ekeanyanwu¹,

Bose²,

Beavers³

et al. 2022

JGIS

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“…The spatial extent of the landslide susceptibility map was then reduced by a factor of safety (FS) analysis to identify and target landslideprone slopes. An FS map was prepared based on input factors used in the Geographic Information System Tool for Infinite Slope Stability Analysis (GIS-TISSA) [42] and Probabilistic Infinite Slope Analysis model (PISA-m) [43]. The equation for calculating the FS is:…”

Section: Creation Of Slope Stability Mapmentioning

confidence: 99%

Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India

et al. 2020

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Persistent Scatterer Interferometry (PSI) techniques are now well established and accepted for monitoring ground displacements. The presence of shallow-seated landslides, ubiquitous phenomena in the tropics, offers an opportunity to monitor and map these hazards using PSI at the regional scale. Thus, the Western Ghats of India, experiencing a tropical climate and in a topographically complex region of the world, provides an ideal study site to test the efficacy of landslide detection with PSI. The biggest challenge in using the PSI technique in tropical regions is the additional noise in data due to vegetation. In this study, we filtered these noises by utilizing the 95-percentile of the highest coherence data, which also reduced the redundancy of the PSI points. The study examined 12 landslides that occurred within one of the three temporal categories grouped as Group 1, Group 2, and Group 3, categorized in relation to PSI monitoring periods, which was also further classified into east- and west-facing landslides. The Synthetic Aperture Radar (SAR) data is in descending mode, and, therefore, the east-facing landslides are characterized by positive deformation velocity values, whereas the west-facing landslides have negative deformation values. Further, the landslide-prone areas, delineated using the conventional factor of safety (FS), were refined and mapped using PSI velocity values. The combination of PSI with the conventional FS approach helped to identify exclusive zones prone to landslides. The main aim of such an attempt is to identify critical areas in the unstable category in the map prepared using FS and prioritizing the mitigation measures, and to develop a road map for any developmental activities. The approach also helps to increase confidence in the susceptibility mapping and reduce false alarms.

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“…The literature features the use of various quantitative methods, particularly physics‐based and data‐driven models, to predict regional spatial landslides. Physics‐based models are linked directly to the environmental characteristics of sites (Escobar‐Wolf et al, 2021; Formetta et al, 2016; Ho & Lee, 2017; Montgomery & Dietrich, 1994). From slope stability and landslide formation, physics‐based models can generate results using detailed field observations of the surface and subsurface landslide systems, although a large amount of detailed data and multiple tuning parameters are required to ensure reliable prediction results.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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A GIS tool for infinite slope stability analysis (GIS-TISSA)

Cited by 36 publications

References 31 publications

Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

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