Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

Frequency Ratio ◽

Weights Of Evidence ◽

Annual Rainfall ◽

Slope Aspect ◽

Equal Proportion ◽

Susceptibility Maps ◽

Abstract Landslide susceptibility mapping is an important tool for disaster management and development activities such as planning of transportation infrastructure, settlement and agriculture. Shikoku Island, which is found in the southwest of Japan, is one of the most landslide prone areas because of heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models were applied in a GIS environment to prepare the landslide susceptibility maps of this area. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were carried out and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models with two, two and one were attempted respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points is slightly better than FRD and WoE models in predicting the future probability of landslide occurrence.

Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

10.21203/rs.3.rs-37349/v2 ◽

2020 ◽

Author(s):

Matebie Meten ◽

Netra Prakash Bhandary

Keyword(s):

Logistic Regression ◽

Frequency Ratio ◽

Weights Of Evidence ◽

Annual Rainfall ◽

Slope Aspect ◽

Susceptibility Assessment ◽

Landslide Susceptibility Assessment ◽

Susceptibility Maps ◽

Abstract Landslide susceptibility assessment is an important tool for disaster management and development activities. Shikoku Island in the southwest Japan is one of the most landslide prone areas due to heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. The objective of this study is to apply Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models in a GIS environment to prepare the landslide susceptibility maps of this area and select the best one for future infrastructure and landuse planning. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were applied and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models resulting two, two and one LSMs respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points was slightly better than FRD and WoE models in predicting the probability of future landslide occurrence.

A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

Geosciences Journal ◽

10.1007/s12303-015-0026-1 ◽

2015 ◽

Vol 20 (1) ◽

pp. 117-136 ◽

Cited By ~ 79

Author(s):

Liang-Jie Wang ◽

Min Guo ◽

Kazuhide Sawada ◽

Jie Lin ◽

Jinchi Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Decision Tree ◽

Comparative Study ◽

Frequency Ratio ◽

Weights Of Evidence ◽

Susceptibility Maps ◽

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽

10.3390/e20110884 ◽

2018 ◽

Vol 20 (11) ◽

pp. 884 ◽

Cited By ~ 28

Author(s):

Tingyu Zhang ◽

Ling Han ◽

Wei Chen ◽

Himan Shahabi

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Shaanxi Province ◽

Slope Aspect ◽

Support Vector ◽

Operating Characteristics ◽

Study Region ◽

Susceptibility Maps ◽

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

Open Geosciences ◽

10.1515/geo-2019-0056 ◽

2019 ◽

Vol 11 (1) ◽

pp. 708-726

Author(s):

Zorgati Anis ◽

Gallala Wissem ◽

Vakhshoori Vali ◽

Habib Smida ◽

Gaied Mohamed Essghaier

Keyword(s):

Vegetation Index ◽

Satellite Image ◽

Susceptibility Mapping ◽

Weight Of Evidence ◽

Slope Aspect ◽

Susceptibility Maps ◽

North Western ◽

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽

10.3390/sym12030325 ◽

2020 ◽

Vol 12 (3) ◽

pp. 325 ◽

Cited By ~ 20

Author(s):

Guirong Wang ◽

Xinxiang Lei ◽

Wei Chen ◽

Himan Shahabi ◽

Ataollah Shirzadi

Keyword(s):

Radial Basis Function Network ◽

Slope Angle ◽

Susceptibility Mapping ◽

Training Dataset ◽

Validation Dataset ◽

Slope Aspect ◽

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.

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Entropy ◽

10.3390/e21040372 ◽

2019 ◽

Vol 21 (4) ◽

pp. 372 ◽

Cited By ~ 12

Author(s):

Zhongjun Ma ◽

Shengwu Qin ◽

Chen Cao ◽

Jiangfeng Lv ◽

Guangjie Li ◽

...

Keyword(s):

Northeast China ◽

Susceptibility Mapping ◽

Changbai Mountain ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Mountain Area ◽

Susceptibility Maps ◽

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.

Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh

Geosciences ◽

10.3390/geosciences10120483 ◽

2020 ◽

Vol 10 (12) ◽

pp. 483

Author(s):

Yasin Wahid Rabby ◽

Yingkui Li

Keyword(s):

Logistic Regression ◽

Regional Scale ◽

Safe Zone ◽

Susceptibility Mapping ◽

Prediction Rate ◽

Susceptibility Maps ◽

Integrated Methods ◽

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS

Engineering Geology ◽

10.1016/j.enggeo.2011.09.011 ◽

2012 ◽

Vol 124 ◽

pp. 12-23 ◽

Cited By ~ 162

Author(s):

Jaewon Choi ◽

Hyun-Joo Oh ◽

Hong-Jin Lee ◽

Changwook Lee ◽

Saro Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Frequency Ratio ◽

Network Models ◽

Neural Network Models ◽

Susceptibility Maps ◽

Artificial Neural Network Models ◽

A Novel Performance Assessment Approach using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest

Sensors ◽

10.3390/s19183940 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3940 ◽

Cited By ~ 26

Author(s):

Sevgen ◽

Kocaman ◽

Nefeslioglu ◽

Gokceoglu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Performance Assessment ◽

Machine Learning Algorithms ◽

Susceptibility Maps ◽

Surface Models ◽

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.