GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods

Köse, Derya Dilara; Türk, Tarık

doi:10.1080/02723646.2018.1559583

Cited by 30 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study district, slope aspect has firm connections with solar radiation, vegetation coverage, rainfall and so on, which could affect slope stability to some extent (Kose and Turk, 2019). In the present study, slope aspects within Jiange County were reclassified as: flat, east, north, northeast, south, southeast, southwest, northwest, west.…”

Section: Fig1 Study Areamentioning

confidence: 80%

“…In some regions, landslide hazards are inclined to spread along rivers, because rivers could erode slope toes and raise moisture content in slope bodies (Clapuyt et al, 2019). The tasks in regard to landslide susceptible mapping, the effects about rivers can be weighed by distance to rivers and river density (Arabameri et al, 2019;Kose and Turk, 2019). During the present research, distance to rivers was selected to construct landslide susceptibility model.…”

Section: Fig1 Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree (FT), support vector machine (SVM) and classification regression tree (CART) and were integrated with bagging strategy. Then these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation (CAE) method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve (ROC) and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. among them the Bag-CART model has the highest AUC value of 0.874, however the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

show abstract

Section: Fig1 Study Areamentioning

confidence: 80%

Section: Fig1 Study Areamentioning

confidence: 99%

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Elevation has connection with landslide occurrence [29], especially in plateau area. Slope angle can affect the slope stability, and the slope aspect controls the hours of sunshine and the effects of rainfall, moisture, and wind conditions over the study area [30]. Plane curvature and profile curvature are morphological factors, which control the water flow on Earth surface affecting landslide occurrences [31].…”

Section: Influencingmentioning

confidence: 99%

“…e erosion by the river to the bank can reduce the strength of the soil and make the slope less stable, which is a positive correlation with the distance to river [30]. e existence of fault makes the rock fragmented and increase the probability of slope instability [30,32]. Stratum lithology is the material basis of landslides, which is also an influencing factor of slope stability [32].…”

Section: Influencingmentioning

confidence: 99%

“…Stratum lithology is the material basis of landslides, which is also an influencing factor of slope stability [32]. Vegetation coverage index and terrain humidity index are important environmental factors and are associated with the structure of soil, which are frequently used in mapping the landslide susceptibility [30]. e Jenks natural breaks method is a one-dimensional clustering algorithm.…”

Section: Influencingmentioning

confidence: 99%

See 1 more Smart Citation

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Wang

Chen

2021

Advances in Civil Engineering

View full text Add to dashboard Cite

Landslides are one of the nature hazards causing a lot of casualties and property losses in the world. Over the last decades, many researchers have made contributions in landslide susceptibility maps using qualitative and quantitative methods. Parameters of DEM, geology, etc. are selected to analyze the mechanism of landslides. The quality of data is essential in the landslide studies, and more credible results can be obtained if the data is adequate and accurate from the wide range of parameters. The aim of this study is to evaluate the landslide susceptibility of Huangyuan County of Qinghai. Through field investigations, 100 landslide disaster locations in the study area were selected, and 11 influencing factors including elevation, slope, aspect, plane curvature, profile curvature, road distance, river distance, fault distance, stratum rock property, vegetation coverage index, and terrain humidity index were selected as the influencing factors of landslide disaster based on GIS. In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. The results show that factors that have great influence on landslides are associated with witness, and the terrain humidity index has the highest weight in the occurrences of landslide. The values of AUC indicate that the ANN model is the best evaluation model suitable for the study area and can be extremely useful for landslide hazard mitigation strategies. Based on the calculation of ANN model, three valley areas are determined with high landslide susceptibility, and necessary reinforcement measures should be taken.

show abstract

Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models

Chen

Yang

2023

Bull Eng Geol Environ

View full text Add to dashboard Cite

GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods

Cited by 30 publications

References 37 publications

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models

Contact Info

Product

Resources

About