Assessment of susceptibility to landslides through geographic information systems and the logistic regression model

Riegel, Roberta Plangg; Alves, Darlan Daniel; Schmidt, Bruna Caroline; Oliveira, Guilherme Garcia de; Haetinger, Claus; Osório, Daniela Montanari Migliavacca; Rodrigues, Marco Antônio Siqueira; Quevedo, Daniela Müller de

doi:10.1007/s11069-020-03997-8

Cited by 24 publications

(9 citation statements)

References 30 publications

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“…The first paper that applied statistical validation methods was published in 2015 [52]. Between 2015 and 2021, 27 articles were published and 15 out of them applied cross tabulation [36,52], success curve [26], AUC [14,16,25,35,40,42], ROC [43,46], or AUC/ROC [15,34,44,45] to validate the susceptibility maps. Two publications mentioned validation curves, but did not provide details about them [13,50].…”

Section: Validation Methodsmentioning

confidence: 99%

“…Some of the publications highlight the importance of the developed work and the application of landslide susceptibility studies in Brazil. The authors discuss how the work can be used by research centers, universities, government, and other institutions interested in the prevention, monitoring, and forecasting of landslides [12,15,29,31,[33][34][35][36][37]41,43,44,46,50,52]. Vieira et al [28] mention the importance of models to provide relevant information about areas susceptible to landslides in a cost-efficient manner.…”

Section: Validation Methodsmentioning

confidence: 99%

“…Out of the 33 studies analyzed, 19 of them were in the southeast region, in the states of São Paulo (10) [15,[25][26][27][28][29][30][31][32][33], Rio de Janeiro (7) [12,[34][35][36][37][38][39], Minas Gerais (2) [16,40], and Espírito Santo (1) [41]. The remaining 14 were in the south region, in Rio Grande do Sul (8) [14,35,[42][43][44][45][46][47], Santa Catarina (4) [13,[48][49][50], and Paraná (2) [51,52] (Figure 3). Only the article by Bragagnolo et al [35] had two study areas, one located in Rio Grande do Sul and the other in Rio de Janeiro state.…”

Section: Location Of Study Areasmentioning

confidence: 99%

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Landslide Susceptibility Mapping in Brazil: A Review

2021

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Landslide susceptibility studies are a common type of landslide assessment. Landslides are one of the most frequent hazards in Brazil, resulting in significant economic and social losses (e.g., deaths, injuries, and property destruction). This paper presents a literature review of susceptibility mapping studies in Brazil and analyzes the methods and input data commonly used. The publications used in this analysis were extracted from the Web of Science platform. We considered the following aspects: location of study areas, year and where the study was published, methods, thematic variables, source of the landslide inventory, and validation methods. The susceptibility studies are concentrated in Brazil’s south and southeast region, with the number of publications increasing since 2015. The methods commonly used are slope stability and statistical models. Validation was performed based on receiver operating characteristic (ROC) curves and area under the curve (AUC). Even though landslide inventories constitute the most critical input data for susceptibility mapping, the criteria used for the creation of landslide inventories are not evident in most cases. The included studies apply various validation techniques, but evaluations with potential users and information on the practical applicability of the results are largely missing.

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Section: Validation Methodsmentioning

confidence: 99%

Section: Validation Methodsmentioning

confidence: 99%

Section: Location Of Study Areasmentioning

confidence: 99%

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Landslide Susceptibility Mapping in Brazil: A Review

2021

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“…The training data were used for the development of the model and the validation data were used for the selection of the model. Here, we used the receiver operating characteristic curve (ROC) and area under curve (AUC) to evaluate and compare the effects of different feature modes on the LS map [97,98]. The algorithm used in this study included binary classification, confusion matrix, and statistical indicators (i.e., precision, accuracy), which were adopted to quantitatively compare the accuracy of LS assessment models [99].…”

Section: Validation Of Ls Assessment Frameworkmentioning

confidence: 99%

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

Qiang

Singh

et al. 2022

Remote Sensing

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Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.

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“…The number of people settling on the slopes of the hills has increased, hence putting a lot of pressure on the environment, weakening it and making it prone to landslides [4,5]. Human activities and natural factors have led to a number of geological disasters, which are a danger, not only to the lives of people, but also to their property [3,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

Nabukonde

Barakagira

Akwango

2023

ACRI

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Landslides, whether induced by nature, or human activities, are one of the most prominent disasters which are of great concern in the world. They cause a lot of havoc to the environment hence a necessity to avoid them whenever possible. This literature review is basically aimed at finding out how Geographical Information System (GIS) and Remote Sensing (RS) technologies are used in avoiding landslides and risks associated with them at different levels; Challenges encountered while using GIS and RS techniques for landslide disaster risk reduction are identified; and therefore, dealing with them requires the involvement of developed countries who have the capacity to provide the necessary equipment to the developing countries that are faced with disasters. This study discusses the use of GIS and RS in mitigation of risks from landslides, and mainly points out how these techniques are applied to avoid disaster risks. Secondary data was reviewed from journal articles, institutional reports, and online publications from similar studies. GIS and RS tools are important in predicting, monitoring and managing landslide disasters. It was concluded that GIS and RS tools provide cheaper, reliable, and faster techniques of accessing spatial data in a given area, therefore regarded as essential technologies that may be necessary for predicting landslide occurrences, these technologies need to be considered in communities which are prone to landslides.

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Assessment of susceptibility to landslides through geographic information systems and the logistic regression model

Cited by 24 publications

References 30 publications

Landslide Susceptibility Mapping in Brazil: A Review

Landslide Susceptibility Mapping in Brazil: A Review

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

The Use of Geographical Information System (GIS) and Remote Sensing (RS) Technologies in Generation of Information Used to Mitigate Risks from Landslide Disasters: An Application Review

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