Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran

Aghda, Seyed Mahmoud Fatemi; Bagheri, Vahid; Razifard, M.

doi:10.1007/s10706-017-0365-y

Cited by 24 publications

(9 citation statements)

References 19 publications

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“…The SVM method uses statistical learning theory to find an optimum linear hyper-plane to separate two classes of landslide and non-landslide. In this study, the nonlinear data is converted into the linearly separable data in a high-dimensional feature space using the radial basis function (RBF) kernel function (KF) [66]. Recent works reported by Reichenbach, et al [67] and Huang and Zhao [68] point out an increasing trend of applying GIS-based machine learning models for landslide susceptibility mapping.…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

et al. 2018

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This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.

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Section: Introductionmentioning

confidence: 99%

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

et al. 2018

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“…This variable might be used straightforward as a potential contributing factor to rockfall; however, the relationship between elevation and landslide hazard is not clear [45]. On the contrary, slope ( 1 ) has been found to be one of the most relevant factors for estimating these phenomena [46], since steeper slopes commonly result in higher stresses in the terrain [6]. Other DEM-derived variables, such as slope aspect or curvature, have been reported to provide a weak predictive ability [47] and were therefore discarded henceforth.…”

Section: B Description Of the Factors Contributing To Rockfall Hazardmentioning

confidence: 99%

“…of other topographic, geologic and hydrologic factors contributing to generating slope instabilities [6]. In consequence, the management and processing of data to characterise these factors is crucial to properly evaluate and model the hazards represented by these phenomena [7].…”

mentioning

confidence: 99%

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Jato-Espino

Roldán-Valcarce

Collazos-Arias³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of Climate Change whereby the effects of these phenomena might be exacerbated. Under this premise, this research concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favour rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modelling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold and precipitation to explain the occurrence of these events. The effects of Climate Change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.

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“…The increasing use of machine learning method was due to robustness and high generalization capability for landslide susceptibility analysis [7]. Machine learning methods including artificial neural network [13], fuzzy logic [14], [15], support vector machine [11], [16], random forest [7], [17], and decision tree [18]- [20] methods have been popularly applied among the others. The present study proposed the new concept to take landslide release area -as a source of landslide occurrence -into account to accurately produce a landslide release susceptibility map by considering nine topographical factors as landslide factors.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

Darminto¹,

Widodo²,

Alfatinah³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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Landslide, one of the most disastrous natural hazards, causes damage to infrastructure worldwide and local communities. Pacitan, Indonesia is one city with high susceptibility to landslides occurrence. The conditions of landslide occurrence are assumed to be the same in the future. This study's objective is to produce a landslide susceptibility map by using machine learning methods based on topographical factors including elevation, slope, aspect, profile curvature, plan curvature, Topographic Wetness Index (TWI), distance to the river, and geological map as independent variables, whereas the landslide inventory map derived from Sentinel-2A and Landsat 7 were used as the dependent variables in the model construction. This study's datasets were constructed in three different compositions where each composition was treated as input in Random Forest, Decision Tree, and Logistic regression model. The first dataset was composed of a 70:30 ratio for training and testing sample points, the second dataset with a 60:40 ratio, and the third with a 50:50 ratio. The performance of each model using each dataset composition was analyzed using various accuracy assessments. This study also considered each topographical factor's effect on model performance by excluding several factors in model construction. From the results, random forest with the first dataset appeared to give the best performance for mapping landslide susceptibility area, shown by the highest Area Under Curve (AUC) value, Coefficient Correlation (CC), and Cohen's Kappa of 0.96, 0.92 (92%) and 0.84, respectively. Elevation and geological maps were considered as essential variables shown by significant drops in model accuracy assessment when these two factors were separately excluded, while profile curvature was the least essential variable based on the insignificant drop in the model accuracy assessment result.

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Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran

Cited by 24 publications

References 19 publications

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

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