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 Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)

2020 ◽  
Vol 17 (12) ◽  
pp. 4206 ◽  
Author(s):  
Yue Wang ◽  
Deliang Sun ◽  
Haijia Wen ◽  
Hong Zhang ◽  
Fengtai Zhang
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Coincidence Degree ◽  
Susceptibility Mapping ◽  
Machine Learning Techniques ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Entire Area ◽  
Conditioning Factors

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

scholarly journals A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shuai Zhao ◽  
Zhou Zhao
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Support Vector

The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

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.

scholarly journals GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

2020 ◽  
Vol 42 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Dang Quang Thanh ◽  
Duy Huu Nguyen ◽  
Indra Prakash ◽  
Abolfazl Jaafari ◽  
Viet -Tien Nguyen ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Conditioning Factors ◽  
Prone Area

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.

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