Landslides Susceptibility Mapping Using Frequency Ratio Model and GIS in Central Parts of Badakhshan Province, Afghanistan

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.

This article emphasizes landslide susceptibility mapping along Ghat road of Kolli hills, Tamil Nadu, India, using frequency ratio, relative effect and fuzzy gamma operator models with the help of remote sensing data and GIS technique. The purpose of the study is to generate, compare and validate landslide susceptibility zones. Landslide inventory was done with data collected from the State Highways department. There are nine landslide-influencing parameters such as slope gradient, slope aspect, slope curvature, relief, lithology, land use and land cover, proximity to road, proximity to drainage, and proximity to lineament, analyzed with help of topo map, existing geology map and satellite data to produce landslide susceptibility maps. Landslide susceptibility maps were generated by calculating relationship between the landslide-influencing factors with past landslide locations using frequency ratio, relative effect and fuzzy gamma operator models. These landslide susceptibility maps were verified and compared using the existing landslide inventory data. The prediction accuracy of frequency ratio model was 87.93 %, for fuzzy gamma operator model was 87.33 %, and for relative effect model it was 85.26 %. Out of which, the frequency ratio model provide maximum prediction accuracy on landslide susceptibility.

The main goal of this study was to produce landslide susceptibility mapping by frequency ratio (FR), statistical index (SI), and index of entropy (IOE) models based on geographic information system (GIS) for the Baozhong region of Baoji, China. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out a field survey. A total of 79 landslides were mapped and out of which, 55 (70 %) were randomly selected for building landslide susceptibility models, while the remaining 24 (30 %) were used for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using FR, SI, and IOE models. Finally, the validation of landslide susceptibility map was carried out using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map using FR model has the highest prediction accuracy of 82.49 %, followed by the SI model (81.43 %) and the IOE model (79.62 %). Similarly, the AUC plot showed that the success rate of the three models was 84.95 % for FR model, 82.37 % for SI model, and 82.05 % for IOE model, respectively. According to the validation results of the AUC evaluation, the map produced by the FR model exhibits the most satisfactory properties. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Landslides susceptibility maps were constructed in Seorak mountain area, Korea, using an integration of frequency ratio and adaptive neuro-fuzzy inference system (ANFIS) in geographical information system (GIS) environment. Landslide occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models, respectively. Topography, geology, soil, and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for landslide susceptibility mapping in the study area. Two landslide susceptibility maps were prepared using the different MFs. The frequency ratio model was also applied to the landslide susceptibility mapping for comparing with the probabilistic ANFIS model. Finally, the resulting landslide susceptibility maps were validated using the landslide locations which were not used for training the ANFIS. The validation results showed 75.57 % accuracy using the generalized bell-shaped MF model, 74.94 % accuracy using the Sigmoidal 2 MF model and 73.07 % accuracy using frequency ratio model. These accuracy results show that an ANFIS can be an effective tool in landslide susceptibility mapping.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

The hilly region of Manipur especially along the NH-39 road, which is the lifeline of the State, is prone to landslides every year particularly during the monsoon season. Anthropological factors, such as excessive deforestation, unsystematic changes in land use and land cover pattern and slope cultivation, etc. are indirectly initiate the process of landslides. In the present study, landslide susceptibility mapping was carried out using frequency ratio and fuzzy gamma operator models with the help of geomatics techniques. The landslide susceptibility mapping was prepared using landslide inventory data and nine landslide causative factors, i.e. lithology, land use and land cover, geomorphology, drainage density, lineament density, slope gradient, slope aspect, curvature, and elevation. These causative factors were prepared with the help of toposheet, high resolution IRS P6 LISS IV satellite imagery, cartosat DEM data and extensive field work. The landslide susceptibility maps were prepared by calculating the relationship between the landslide causative parameters with landslide areas using a frequency ratio model. To get the fuzzy membership values, the frequency ratio values were normalized between the ranges of 0 and 1. The landslide susceptibility maps were compared and prediction accuracy of both the models was derived using the area under curve (AUC) method. The success rate curves were obtained using both training and all landslide inventory dataset. For training landslide inventory dataset, the AUC value of the success rate curve for the frequency ratio model was found to be 0.8056, whereas for the fuzzy gamma operator (using γ = 0.99) model, it was calculated as 0.9150. In the case of all landslide inventory dataset, the AUC value of the success rate curve for the frequency ratio model and the fuzzy gamma operator model were 0.7921 and 0.8188, respectively. The landslide susceptibility index was also compared with the landslide validation inventory dataset to obtain the prediction rate curves. The AUC value of the prediction rate curve for the frequency ratio model was 0.5681, whereas in the case of the fuzzy gamma operator model, it was 0.6721.

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

This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

The current research presents landslide susceptibility mapping by frequency ratio (FR) and weights of evidence (WoE) models based on geographic information system (GIS) and an assessment of their performances for the Shangzhou District of Shangluo City, China. Firstly, landslide locations of the study area were detected using aerial photographs as well as by carrying outfield survey. Then, a total of 145 landslides were mapped out of which 101 (70 % landslide locations) were randomly selected for training the models, and the remaining 44 (30 % landslide locations) were used for validating the models. The following ten landslide conditioning factors, such as slope aspect, curvature, slope angle, elevation, distance to rivers, distance to faults, lithology, peak ground acceleration, distance to roads and precipitation, were considered in this study. Subsequently, landslide susceptibility maps were produced using FR and WoE models in ArcGIS 10.0 software. The validation of landslide susceptibility maps were carried out using areas under the curve. The validation results showed that the training accuracy were 0.7635 (76.35 %) and 0.7450 (74.50 %) for the FR and WoE models, with predictive accuracy 0.7395 (73.95 %) and 0.7102 (71.02 %), respectively, indicating that landslide susceptibility mapping using FR model is more accurate than WoE model for the study area.

The assessment of landslide susceptibility is closely associated with the spatial distribution of landslides. In the present study, both frequency ratio (FR) model and modified information value (MIV) model were applied to analyse landslide susceptibility in Darjeeling Himalaya. Both the models dealt with the relationship between landslide phenomena and landslide conditioning factors. To perform the models data layers, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were taken into account. Each and every class/category of landslide conditioning factor contributes a relative importance in landslide occurrences. To prepare all the data layers, Landsat TM image, SRTM DEM, Google earth image, and some authorized maps were processed in accordance with ArcMap 10.1 and Erdas imagine 9.2. To obtain the relative significance of each class/category of landslide conditioning factors, frequency ratio (FR) value and modified information value (MIV) were estimated and accordingly the ranking values were assigned to each class/category to integrate all the data layers on GIS platform as well as to prepare landslide susceptibility map of Darjeeling Himalaya. The derived landslides susceptibility maps by using frequency ratio model and modified information value model were verified being considering the area under curve (AUC) of ROC curve and frequency ratio plot. The AUC value of ROC curve of FR model and MIV model was 0.746 and 0.769, respectively. The AUC value represents the prediction accuracy of landslide susceptibility map was 74.6% for frequency ratio model and 76.9% for modified information value model.

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.

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

This study presents a comprehensive landslide susceptibility mapping (LSM) for Wayanad district using a Multi-Criteria Decision-Making (MCDM) approach, integrating Geographic Information Systems (GIS) with the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models. The methodology involves a six-step process: data collection from USGS, SRTM-DEM and Bhukosh followed by the creation of thematic maps covering elevation, slope, aspect, proximity to roads and rivers, geological features, rainfall and land use/land cover. AHP is applied by rescaling thematic maps to a uniform 5-point scale, calculating the consistency index and determining weights. If the consistency ratio (CR) is ≥ 0.10, adjustments are made to ensure accuracy. FR values for each factor are computed to develop the LSM. The LSM was validated using Receiver Operating Characteristic (ROC) curves and Area under the Curve (AUC) values, with AUC scores of 0.913 and 0.896 for the AHP and FR models, respectively indicating high prediction accuracy. The LSM is categorized into five susceptibility classes: very low, low, moderate, high and very high, providing critical insights for disaster preparedness and risk mitigation in Wayanad. The study underscores the significant role of GIS and MCDM techniques in enhancing landslide risk assessment and management.

Landslides and slope instabilities are major risks for human activities which often lead to economic losses and human fatalities all over the world. The main purpose of this study is to evaluate and compare the results of Landslide Nominal Risk Factor (LNRF), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) models in mapping Landslide Susceptibility Index (LSI). The study case, Nojian watershed with an area of 344.91 km2, is located in Lorestan province of Iran. The procedure was as follows: first, the effective factors of the landslide basin were prepared for each layer in the GIS software. Then, the layers and the landslides of the basin were also prepared using aerial photographs, satellite images, and fieldwork. Next, the effective factors of the layers were overlapped with the map of landslide distribution to specify the role of units in such distribution. Finally, nine factors including lithology, slope, aspect, altitude, distance from the fault, distance from river, fault land use, rainfall, and altitude were found to be effective elements in landslide occurrence of the basin. The final maps of LSI were prepared based on seven factors using LNRF, FR, and AHP models in GIS. The index of the quality sum (Qs) was also used to assess the accuracy of the LSI maps. The results of the three models with LNRF (40%), FR (39%), and AHP (44%) indicated that the whole study area was located in the classes of high to very high hazard. The Qs values for the three models above were also found to be 0.51, 0.70 and 0.70, respectively. In comparison, according to the amount of Qs, the results of AHP and FR models have slightly better performed than the LNRF model in determining the LSI maps in the study area. Finally, the study watershed was classified into five classes based on LSI as very low, low, moderate, high, and very high. The landslide susceptibility maps can be helpful to select sites and mitigate landslide hazards in the study area and the regions with similar conditions.

The purpose of this study is to assess the susceptibility of landslides in Wen County, China, using both analytical hierarchy process (AHP) and frequency ratio (FR) models. For this, a total of 529 landslides were identified and randomly split into two groups. The modeling group, which represented approximately 70% of the total landslides, was used as a training set to construct the susceptibility maps. The remaining 30% were used for validation purpose. Eight layers of landslide-related factors were prepared, including slope angle, altitude, distance to rivers, distance to roads, distance to faults, rainfall, lithology, and normalized difference vegetation index. Subsequently, landslide susceptibility maps were produced using the models. For verification, an area under curvature (AUC) and the seed cell area index (SCAI) assessments were applied. The AUC plot estimation results showed that the success rates of the AHP and FR models were 83.55 and 88.42% and the prediction rates were 83.43 and 86.62%, respectively. According to the validation results of the AUC and SCAI evaluations, the map obtained from the FR model is more accurate than that from the AHP model. These landslide susceptibility maps can be used for optimum management by decision makers and land-use planners.