Landslide susceptibility evaluation and hazard zonation techniques – a review

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

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GIS-based Landslide Susceptibility Evaluation Using Analytical Hierarchy Process (AHP) Approach: The Case of Tarmaber District, Ethiopia

Momona Ethiopian Journal of Science ◽

10.4314/mejs.v11i1.2 ◽

2019 ◽

Vol 11 (1) ◽

pp. 14 ◽

Cited By ~ 4

Author(s):

Asmelash Abay ◽

Giulio Barbieri ◽

Kifle Woldearegay

Keyword(s):

Analytical Hierarchy Process ◽

Landslide Susceptibility ◽

Hierarchy Process ◽

Susceptibility Evaluation

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A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Engineering Geology ◽

10.1016/j.enggeo.2009.10.001 ◽

2010 ◽

Vol 110 (1-2) ◽

pp. 11-20 ◽

Cited By ~ 226

Author(s):

A. Nandi ◽

A. Shakoor

Keyword(s):

Landslide Susceptibility ◽

Statistical Analyses ◽

Multivariate Statistical Analyses ◽

Multivariate Statistical ◽

Susceptibility Evaluation

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GIS based Landslides Susceptibility Mapping using Probabilistic Certainty Factor for Hazard Zonation in Mandakini Valley

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7288.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 398-404

Keyword(s):

High Risk ◽

Landslide Susceptibility ◽

Catchment Area ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Hazard Zonation ◽

Certainty Factor ◽

Landslide Susceptibility Map ◽

Risk Zonation

Landslides are highly threatening a phenomenon which is very common in hilly region and mountainous regions. These landslides trigger major risks leading to heavy losses in terms of life and property. Many studies were conducted globally to determine Landslide vulnerability of different locations. In order to assess vulnerability, there were few studies around Landslides Susceptibility mapping also whose main objective is to identify high-risk vulnerable areas, there by applying measure to reduce the damage caused, if it were to happen in near future. In literature, there are many methods available for predictive susceptibility mapping of landslides. However, identification of any of the prevalent method for a specific area require utmost care and prudence because land sliding is a result of complex geo-environmental spatial factors. Mandakini valley is highly ruggedized terrain with intensive rains during monsoon season. As a result, Landslides are very common in the Mandakini River valley and its catchment area. These landslides cause severe damage to human settlements and infrastructure present in this area. In this study, we have used certainty factor method in order to generate landslide susceptibility map for the catchment area of Mandakini river. Certainty factor approach is a bi-variate probabilistic method which uses Geo-environmental parameters like elevation, slope, aspect, rainfall distance away from river, soil characteristics etc. to generate landslide susceptibility map. A Script was developed in ArcPy - a python package to design tools for generating susceptibility map. These tools can run both at desktop level and at server level and generate results in an integrated way. Esri ArcMap 10.7 is used in order to generate required data layers and thematic maps. Overall, this paper leverages GIS technology and its tools to performs Landslide Susceptibility Mapping using Probabilistic Certainty Factor and generate Hazard Zonation of Mandakini Valley using an automated script for generating Landslide Susceptibility Mapping and Hazard Risk Zonation. It was found that out of 696, total 136 villages are under high risk of landsides, total 329 villages are under moderate risks and around 231 villages are under low risk zonation impacting lives of approx. 216166 people. Also, it is worth mentioning that a GIS based script was developed to automate generation of Landslide Susceptibility Maps which can be used where the same geological and topographical feature prevails.

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A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

10.5194/egusphere-egu21-3415 ◽

2021 ◽

Author(s):

Mariano Di Napoli ◽

Diego Di Martire ◽

Domenico Calcaterra ◽

Marco Firpo ◽

Giacomo Pepe ◽

...

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Shallow Landslide ◽

Shallow Landslides ◽

Predisposing Factors ◽

Mitigation Measures ◽

Economic Losses ◽

Meaningful Improvement ◽

Susceptibility Evaluation ◽

Landslide Runout

Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also &#160;landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the &#8220;reach angle&#8221; method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides&#160; in the future and to identify areas where risk mitigation measures are needed.

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