scholarly journals A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along with Forest Road Networks

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1087
Author(s):  
Nastaran Zare ◽  
Seyed Ata Ollah Hosseini ◽  
Mohammad Kazem Hafizi ◽  
Akbar Najafi ◽  
Baris Majnounian ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Road Networks ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Ratio Model ◽  
Forest Road ◽  
Neuro Fuzzy ◽  
Frequency Ratio Model

In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region.

Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping

2012 ◽  
Vol 225 ◽  
pp. 442-447 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Zulkiflee Abd. Latif ◽  
Siti Nur Afiqah Aman
Keyword(s):  
High Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Airborne Lidar ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Landslide Susceptibility Map ◽  
Frequency Ratio Model

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

Assessing landslide vulnerability using bivariate statistical analysis and the frequency ratio model. Case study: Transylvanian Plain (Romania)

2016 ◽  
Vol 60 (4) ◽  
pp. 359-371 ◽  
Author(s):  
Gheorghe Roşian ◽  
Horváth Csaba ◽  
Réti Kinga-Olga ◽  
Cristian-Nicolae Boţan ◽  
Ionela Georgiana Gavrilă
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Slope Angle ◽  
Predisposing Factors ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Statistical Index ◽  
Model Case ◽  
Frequency Ratio Model

Landslides are among the most destructive natural hazards in several regions. Here we summarize our findings regarding this phenomenon in the Transylvanian Plain (Romania) using two susceptibility models: the statistical index and the frequency ratio model. Using Esri's ArcGIS Raster Calculator tool we generated susceptibility maps by summarizing the following twelve landslide predisposition factors: lithology, soil type, fault distance, drainage network distance, roads distance, land use (Corrine Land Cover and NDVI), slope angle, aspect, elevation, plan curvature and soil erosion (RUSLE). The landslide susceptibility has been assessed by computing the values for each class of the predisposing factors and thus evaluating the distribution of the landslide zones within each factor, using Esri's Tabulate Area Tool. The extracted predisposing factors maps have then been re-classified on the basis of the computed values in a raster format. Finally, the landslide susceptibility map has been reclassified into five classes using Natural Breaks (Jenks) classification method. The model performance was assessed with Receiver Operating Characteristic (ROC) curve and the R-index. The models with high number of factors had the lowest accuracy (AUC values being <0.8). The best frequency ratio model (AUC = 0.884) contained only three factors (slope, aspect, elevation) while in the case of the statistical index model the best model (AUC = 0.879) contained four factors (slope, aspect, elevation and NDVI). A significant part (33%) of the study area is characterized by a high to very high degree of susceptibility for landslides.

scholarly journals Integration of Information Theory, Fractal Theory and Statistical Analyses for Landslide Susceptibility Mapping at Regional Scales

2022 ◽  
Author(s):  
Xiaolong Deng ◽  
Guangji Sun ◽  
Naiwu He ◽  
Yonghua Yu
Keyword(s):  
Information Theory ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Fractal Theory ◽  
Spatial Association ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Slope Aspect ◽  
Ratio Method

Abstract A new model, integrating information theory, fractal theory and statistical model for accurate landslide susceptibility mapping (LSM) at regional scales, has been proposed. In this model, landslide conditional factors are firstly classified with an optimal number of classes, which is determined by maximizing their information coefficients estimated from Shannon’s entropy model. The spatial association between influencing factors and induced landslides has been measured by introducing the variable fractal dimension method (VFDM). The VFDM approach fully considers the characteristics of landslide fractal distribution. Then the fractal dimensions (\(D\)) are calculated to provide multiple factors with various numerical weights. The proposed model eventually combines the landslide frequency ratio (\(fr\)) of each factor with corresponding weight to achieve spatial prediction of landslides, illustrated by an example area in China. In the study area, 500 landslides have been identified by aerial photograph interpretation, extensive field investigations, historical and bibliographical landslide data. In the model, these landslides are randomly split into a training dataset (70 %)and a validating dataset (30 %) Seven factors are recognized and analyzed by frequency ratio (FR) method, including lithology, distance to fault, altitude, slope, aspect, distance to stream and distance to the road. The receiver operating characteristic curve (AUROC) has been adopted to compare and validate the model results. Results show that the proposed landslide model achieved a more accurate prediction with AUROC equal to 0.8467, over-performing than the conventional frequency ratio method (AUROC=0.8088). According to the final prognostic landslide susceptibility map, 16.37 % f the study area shows very high and high susceptibility, accounting for 63.55 % f the entire landslides. Evaluation of relative factor importance based on a one-by-one factor removal test indicates that the lithology factor contributes unique information for landslides. In conclusion, the example demonstrates that the proposed framework is promising for further improvement of LSM.

scholarly journals Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Florence Elfriede Sinthauli Silalahi ◽  
Pamela ◽  
Yukni Arifianti ◽  
Fahrul Hidayat
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Slope Aspect ◽  
Gis Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Conditioning Factors ◽  
Landslide Susceptibility Index ◽  
Frequency Ratio Model

Abstract Landslides are common natural disasters in Bogor, Indonesia, triggered by a combination of factors including slope aspect, soil type and bedrock lithology, land cover and land use, and hydrologic conditions. In the Bogor area, slopes with volcanic lithologies are more susceptible to failure. GIS mapping and analysis using a Frequency Ratio Model was implemented in this study to assess the contribution of conditioning factors to landslides, and to produce a landslide susceptibility map of the study area. A landslide inventory map was prepared from a database of historic landslides events. In addition, thematic maps (soil, rainfall, land cover, and geology map) and Digital Elevation Model (DEM) were prepared to examine landslide conditioning factors. A total of 173 landslides points were mapped in the area and randomly subdivided into a training set (70%) with 116 points and test set with 57 points (30%). The relationship between landslides and conditioning factors was statistically evaluated with FR analysis. The result shows that lithology, soil, and land cover are the most important factors generating landslides. FR values were used to produce the Landslide Susceptibility Index (LSI) and the study area was divided into five zones of relative landslide susceptibility. The result of landslide susceptibility from the mid-region area of Bogor to the southern part was categorized as moderate to high landslide susceptibility zones. The results of the analysis have been validated by calculating the Area Under a Curve (AUC), which shows an accuracy of success rate of 90.10% and an accuracy of prediction rate curve of 87.30%, which indicates a high-quality susceptibility map obtained from the FR model.

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