Success Rate Curve Research Articles

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

Landslides are a recurrent environmental hazard in hilly regions and affect the socioeconomic development in Pakistan. The current study area is the tourism and hydro energy hub of Pakistan and is affected by environmental hazard. A landslide susceptibility mapping (LSM) of the Hindu Kush Himalayan, Swat District, Pakistan, can be created to reduce demographic losses due to landslides. This current study is conducted to apply three bivariate models, including weights of evidence (WOE), frequency ratio (FR), and information value (IV) for an LSM that has not been explored or applied in the current study area. For this purpose, first, an inventory map of 495 landslides was constructed from both ground and satellite data and randomly divided into training (70%) and testing (30%) datasets. Furthermore, 10 conditioning factors (elevation, slope, aspect, curvature, fault, rainfall, land use land cover (LULC), lithology, road, and drainage) used for the mapping of landslides were prepared in ArcGIS 10.8. Finally, LSM is generated based on WOE, FR, and IV models and validated the performance of LSM models using the area under receiver operating characteristic curve (AUROC). The findings of success rate curve (SRC) of the WOE, FR, and IV models were 67%, 93%, and 64%, respectively, while the prediction rate curves (PRCs) of the three models were 87%, 95%, and 73%, respectively. The validation results for WOE, FR, and IV justified that the FR model is the most reliable technique of all three of these models to produce the highest accuracy LSM for the present study area. Policymakers can use the findings of current research work to mitigate the loss due to landslide hazard.

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

3DU–TA: A new multifractal model involving surface topography for separation of geochemical anomalies

A novel anomaly recognition technique was propounded to help reconcile mineralization-related geochemical patterns with topographic non-stationaries within the study area. The topography variations were characterized by elevation and slope factors of grid cells in a digital elevation model (DEM). In the first step of the technique architecture, the spatial U-statistics formulation was expanded for a 3D framework to integrate elevation-related information into geochemical measurements. Slope angles were then modeled across the cell-based geochemical map using DEM information to modulate planimetric areas into topographic attributes. Three-dimensional spatial U-values and topographic attributes were integrated in a multifractal model called 3DU–TA, providing power-law relations that aid in decomposition of geochemical patterns. The mathematical formulation established for the 3DU–TA model was implemented by programming in MATLAB platform and it was applied to geochemical data pertaining to a deposit-scale porphyry copper system. Core drilling data were used as benchmark to appraise the performance of the 3DU–TA model in the recognition and separation of metal enrichment levels from background and it was compared with the well-established concentration–area (C–A) fractal model. We defined mineralization rate Mr and normalized productivity Np as criteria for judging the integrity of anomalous geochemical patterns derived by the 3DU–TA model in reflecting subsurface mineralization. We also adapted a success-rate plot for deposit-scale experiments and the relevant areas under the curve (AUC) as gauges for quantitative evaluation of model relevance in reflecting subsurface metal enrichment. The derived values of Mr and Np imply that the geochemical populations decomposed with the 3DU–TA model, compared with those decomposed with the C–A model, yielded anomaly patterns that were more consistent with subsurface metallogenic realities. The success-rate curve for topography-enhanced results had an AUC = 0.940, which was higher than that for the C–A model (AUC = 0.867), indicating that the former revealed anomalies with substantially stronger spatial correlations with subsurface metal contents. Intuitive geochemical and quantitative metallogenic assessments suggest that the proposed multifractal modeling technique is capable of providing more practical insights into mineralization realities in geosystems with complex topographic variations.

Comparing Delphi-fuzzy AHP and fuzzy logic membership in soil fertility assessment: a study of an active Ganga Delta in Sundarban Biosphere Reserve, India.

The present study led to setting up a grid-based soil fertility map along with the best fit model in the coastal regions based on soil physical (coarse, sand, silt, clay, bulk density), chemical (CEC, pH, and soil organic carbon), topographic (elevation), and nutrient elements (P2O5, K2O, Na, Zn, B) in the active Ganga deltaic region of Sundarban Biosphere Reserve, India. Soil samples have been collected from 30 soil grids, and 0-15 cm soil depth was preferred for fertility analysis because most essential soil chemical and nutrient elements affecting soil fertility are concentrated in this depth range. We have used the fuzzy-AHP-Delphi (FAHP) and fuzzy logic-Delphi (FL) methods to determine the soil fertility zone. The rules are generated on the MATLAB interface in the text form; the words "IF," "THEN," "IS," "AND," etc., are used to complete the mode-building process. The weights and the desirable limits for each criterion were set based on the expert opinions and existing literature. The kriging interpolation method and natural break classification were used to represent the soil fertility maps into five classes, namely very high fertility (0.80-1.0), high fertility (0.60-0.80), moderate fertility (0.40-0.60), low fertility (0.20-0.40), and very low fertility (0.00-0.20) respectively. Both the models show that soil fertility is respectively higher near the Hooghly River bank. In many cases, the results obtained from FAHP and FL are quite similar but huge dissimilarity has been noticed in grid numbers G2, G3, G4, F1, and F2. Since the FAHP method has been used for the weight of each criterion, therefore, it only prefers those more important parameters over others. The overall accuracy of the soil fertility map was 82.16% for the fuzzy logic model, and 79.62% for the FAHP model and the kappa coefficient value was determined as 0.82 for the fuzzy logic model and 0.79 for the FAHP model. The soil fertility map was validated using the success rate curve under the ROC technique, and the area under curve (AUC) was calculated as 84.02% for the fuzzy logic model and 81.60% for the FAHP model. Since the standard limits for each criterion were known, therefore, fuzzy logic was found to best fit the model for analyzing soil fertility for each grid.

A Hybrid Physical and Machine Learning Model for Assessing Landslide Spatial Probability Caused by Raising of Ground Water Table and Earthquake in Atsuma, Japan — Case Study

Landslides are catastrophic natural events primed and/or triggered by extreme rainfalls and strong earthquakes. Simultaneous occurrence of rainfall and seismic activity increases the likelihood of landslides. However, the researchers focused on this aspect are not much. In the present research, a hybrid model was developed to predict the landslide occurrences probability in Atsuma, Japan triggered by rainfalls and earthquakes. The proposed model is a combination of a physical and machine learning model for improving the accuracy of the landslide susceptibility mapping. The proposed model consisted of a physical module, a machine learning module and a matrix approach module. The physical module assessed the effects of rainfall and peak ground acceleration (PGA) on landslide occurrence probability based on a pseudo-static model. The machine learning module applied Multilayer Perceptron Neural Networks to assess landslide susceptibility, using 611 landslide events caused by strong earthquakes and extreme typhoons. The landslide susceptibility maps obtained from these two modules were then combined into final susceptibility map through a matrix approach. The final susceptibility map included five susceptible levels: very low, low, moderate, high, and very high. To evaluate the proposed model performance, the resulting models were assessed using the areas under the receiver operating characteristic curves. The areas under the success rate curves from the physical module, machine learning module and matrix-based approach showed 79.2%, 82.7% and 83.9% accuracy, respectively. Furthermore, the predicted rate curves showed that the areas under the curve for physical module, machine learning module and matrix-based approach were 78.4%, 82.3% and 83.4%, respectively. These results suggest that the proposed hybrid model improves the prediction capability compared to physically-based method or machine learning model and can be readily used to assess spatial probability of landslide.

Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China

Random forest (RF) and MaxEnt models are shallow machine learning approaches that perform well in predicting species' potential distributions. RF models can produce robust results with the default automatic configuration in most cases, but it is necessary for MaxEnt to optimize the model settings to improve the performance, and the predictive performance difference between optimized MaxEnt and RF is uncertain. To explore this issue, the potential distribution of the endangered amphibian Quasipaa boulengeri in China was predicted using optimized MaxEnt and RF models. A total of 408 occurrence data were selected, 1000 locations were generated as pseudo-absence data by the geographic distance method, and 10,000 sites were selected as background data by creating a bias file. Partial ROC at different thresholds and success rate curves were used to compare the predictive performances between optimized MaxEnt and RF. Our results showed that the RF and optimized MaxEnt models both had good performance in predicting the potential distribution of Q. boulengeri, with the RF model performing slightly better whether based on partial ROC or success rate curves. Furthermore, the core suitable habitat regions of Q. boulengeri identified by RF and MaxEnt were similar and were all located in the Sichuan, Chongqing, Hubei, Hunan, and Guizhou provinces. However, the RF model produced a habitat suitability map with higher discrimination and greater heterogeneity. Temperature annual range, mean temperature of the driest quarter, and annual precipitation were the vital environmental variables limiting the distribution of Q. boulengeri. The RF model is the stronger machine learner. We believe it may be more applicable in predicting the native potential distributions of species with sufficient occurrence data, given the additional predictive detail, the simplicity of use, the computational time involved, and the operational complexity.

Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County

Landslides are one of the most widespread disasters and threaten people’s lives and properties in many areas worldwide. Landslide susceptibility mapping (LSM) plays a crucial role in the evaluation and extenuation of risk. To date, a large number of machine learning approaches have been applied to LSM. Of late, a high-level convolutional neural network (CNN) has been applied with the intention of raising the forecast precision of LSM. The primary contribution of the research was to present a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM), logistic regression (LR), and random forest (RF). Subsequently, we used this model in the Wenchuan region, where a catastrophic earthquake happened on 12 May 2008 in China. There were 405 valuable landslides in the landslide inventory, which were divided into a training set (283 landslides) and validation set (122 landslides). Furthermore, 11 landslide causative factors were selected as the model’s input, and each model’s output was reclassified into five intervals according to the sensitivity. We also evaluated the model’s performance by the receiver operating characteristic (ROC) curve and several statistical metrics, such as precision, recall, F1-score, and other measures. The results indicated that the CNN-based methods achieved the best performance, with the success-rate curve (SRC) and prediction-rate curve (PRC) approaches reaching 93.14% and 91.81%, respectively. The current research indicated that the approach based on the CNN for LSM had both outstanding goodness-of-fit and excellent prediction capability. Generally, the LSM in our research is capable of advancing the ability to assess landslide susceptibility.

GIS-based assessment of landslide susceptibility and inventory mapping using different bivariate models

The frequency ratio (FR) and Shannon entropy (SE) two bivariate models are used to produce LSM for CHA, Bangladesh, and a comparison is established between the two models through validation. A landslide inventory map of CHA with 730 landslides was randomly dispersed into the testing dataset, with 70% (511 landslides) used for modeling and 30% (219 landslides) used for validation processes. The LSM shows that very high LS, high LS, moderate LS, low LS, and very low LS regions. The validity of the two methods was determined using the receiver operating characteristic curve and the area under the curve. The AUC for the success rate curve findings is 0.8437 for FR and 0.7962 for SE. Furthermore, the prediction rate curve accuracy for FR was 0.8364, whereas it was 0.8324 for SE. In calculating the LSM in the study region, the FR model is the more accurate of the two models.

Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

The Carajás Mineral Province (CMP) located in the southeast of the Amazon craton, northern Brazil is one of the most important provinces of the world hosting a high number of large-tonnage Cu–Au deposits. This potential is due to overprinted mineralization events dated from the Neoarchean to Paleoproterozoic, including VMS, IOCG, and Cu–Au granite-related systems. The discovery of a new mineral deposit is linked to direct detection methods that have an elevated cost. Mineral potential modeling is a multi-source geoscience data that can help outline high prospective areas, containing new mineral exploration targets in the order to reduce the exploration risk. This work employed one knowledge-based method, multi-class index overlay, and one machine-learning algorithm, random forest, to produce two mineral potential maps in a district scale for Cu–Au deposits in the easternmost CMP (Parauapebas area) using a mineral system-based approach. This approach consists of translating the critical ore-forming processes of the mineral system components (source of fluids, metals, and ligands, source of energy, pathways, ore deposition gradient) into spatial data (evidence maps). For this study, ten evidence maps were created using the Spatial Data Analyst in GIS, then for the multi-class index overlay, each evidence map received a weight according to its relevance in the modeling, and integrated through the Image Analyst Tools. For the random forest algorithm, meanwhile, the model was trained four times using the Orange Data Mining software. The performance of the models was measured through the classification of accuracy, ROC curve, AUC values, predictive and success-rate curves. Both models achieve good performance with classification accuracy and AUC values of 80% and 0,805 for the multi-class index overlay and values of 76,4% and 0,812 for the random forest. Although the models have similar performance, the analysis of the curves showed that the random forest model was able to predict 82% of the locations of the known deposits, selecting about 25% of the total study area, while the multi-class index overlay predicted 72% of the location of the known deposits over 35% of the study area. Furthermore, both models predicted less than 10% of the study area as highly prospective helping prioritize areas for research and follow-up copper and gold exploration activities.

Using GIS and remote sensing for mapping land sensitivity to wind erosion hazard in the middle Moulouya Basin (North-Eastern Morocco)

Wind erosion is one of the most serious forms of land degradation in arid and semi-arid regions because it affects agricultural land, which in turn leads to a decrease in the quality of the soil leading to economic losses. As a perfect area of study, the northeastern region of the Kingdom of Morocco, specifically the Moulouya Basin, is considered the first on the list of areas threatened by this alarming environmental phenomenon. This study aims to apply the fuzzy logic approach, remote sensing data, and geographic information systems (GIS) to analyze wind erosion and sensitivity mapping data in the basins of Middle Moulouya and Guercif. To this end, the reliance is based on three causative factors obtained using fuzzy logic approach functions for each parameter: soil, vegetation, and climate. Furthermore, the fuzzy operator “GAMMA” was applied to create a wind erosion sensitivity map. The success rate curves revealed that the fuzzy gamma factor (y), with Y = 0.9, gave the best predictive accuracy of 85.2% for the area under the curve. The wind erosion sensitivity map includes areas with regions of five relative sensitivity classes: very high, high, moderate, low, and very low. The estimated result was verified by field measurements and the statistically significant high value of the chi-square test.

GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya

ABSTRACT The main sought of this study is to assess the landslide susceptibility map (LSM) of the East Sikkim Himalaya based on the comparative model analysis using frequency ratio (FR), logistic regression (LR), random forest (RF) and integration of analytical hierarchy process (AHP) with FR (AHP-FR). The models were trained by 166 landslides (70% training) and 12 landslide causative factors whilst tested with the help of 71 landslides (30% testing). Their spatial correlation between the landslides and the causative factors was analysed by using a multicollinearity test. The generated LSM was classified into five classes, i.e. very low, low, moderate, high and very high. In East Sikkim, very high classes of the AHP-FR, LR and FR models cover the area of 11.97%, 11.99% and 7.13%, respectively. The accuracy of prepared LSM was evaluated by using the success rate curve (SRC), prediction rate curve (PRC) and seed calculation area index (SCAI). The area under the curve (AUC) of the success rate curve is 0.88 for the RF model, 0.85 for AHP-FR, 0.78 for LR and 0.79 for FR, whilst the prediction rate curve is 0.86% for the RF model, 0.81 for AHP-FR, 0.79 for LR and 0.78 for FR. The SCAI values of very high susceptibility classes are 0.14, 0.17, 0.18 and 0.19 for the RF, AHP-FR, LR and FR models, respectively. The RF and integrated AHP-FR methods indicate better results as compared to other statistical models.

GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya.

Landslides are one the most destructive and life-endangering hazard in the Darjeeling Himalayan region and keeping in mind the interest of society and their future prospects identification of landslide potential areas is a very pertinent task in this area. Therefore, the present study aimed toward the landslide susceptibility zonation (LSZ) mapping in and around the Kalimpong region by applying Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors including slope, lineament, drainage density, land use land cover, relative relief, soil texture, lithology, elevation, aspect, thrust and faults, plan curvature, profile curvature, road network, topographic wetness index and stream power index. Tolerance and variance inflation factors with Pearson's correlation coefficient are used to assess potential collinearity among the selected factors, and subsequently, the final model has been constructed by enduring an acceptable consistency ratio (<0.10). Thereafter, to classify this region into very low, low, moderate, high and very high susceptible zones quantile, geometric interval, Jenk's natural break and success rate curve (SRC) techniques are adopted to compare and check the optimum LSZ categorization. Considering the identified 647 landslides, Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve is used to gauge the best LSZ map. The AUC ROC shows SRC method (m = 0.9) yields the highest result, achieving a prediction accuracy of 79.5% and, therefore, is considered the most promising LSZ form for the present study area. The results obtained from the study highlight the spatial information of areas that may face slope instability and helps government agencies, stakeholders for drafting adequate measures due to absence of proper landslide early warning systems in this region.

Multi-Hazard Susceptibility Assessment Using the Analytical Hierarchy Process and Frequency Ratio Techniques in the Northwest Himalayas, Pakistan

Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) in the Geographic Information System (GIS). The results reveal that the most suitable areas are distributed in the southern and northwestern parts, which can be used for future sustainable development and other economic activities. In contrast, the eastern and western regions, including Muzaffarabad City, are within high and very susceptibility zones. Finally, more than 50% of the land area is located in very low and low susceptibility zones. The validation of the proposed model was checked by using three different techniques: the Receiver Operative Characteristic (ROC) curve, Seed Cell Area Index (SCAI), and Frequency Ratio (FR). Both ROCs, the Success Rate Curve (SRC) and the Predictive Rate Curve (PRC), showed the goodness of fit for both the selected geo-hazards: landslides (81.3%) and floods (93.2%), at 80.1% and 91.7%, respectively. All the validation techniques showed good fitness for both the individual and multi-hazard maps. The proposed model sets a baseline for policy implementation for all the stakeholders to minimize the risk and sustainable future development in areas of high frequent geo-hazards.

Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran

In this paper, in order to reveal the regional geochemical patterns of regularly sampled stream sediment data, we have employed the K-means and self-organizing map (SOM) as clustering methods in the Moalleman district, northeast Iran. Initially, a set of analyzed elements of geochemical data was subjected to isometric log-ratio (ilr) transformation to address the closure problem related to geochemical data, then, ordinary principal component analysis (PCA) was utilized for recognizing the internal relations between selected elements (As, Au, Cu, Pb, Sb and Zn). Subsequently, the K-means and SOM as unsupervised clustering methods were applied based on PC1 (Cu-Pb-Zn aggregation) and PC2 (Au-As-Sb aggregation) to distinguish different populations of multi-element geochemical indicators. In this regard, Silhouette Width (SW) was implemented for computing the optimal cluster number in K-means clustering method. In the next step, due to the presence of numerous copper mineral deposits/occurrences in the study area, we opted to implement the supervised SOM on ilr-transformed values of Cu-Pb-Zn elements for delineating high anomalous zones. For this purpose, a confusion matrix based on training and out-of-bag (OOB) data was developed for the supervised SOM model and the results indicated the accuracy of 96.27% and 94.26%, respectively. Moreover, success-rate curves were used for assessing the overall performance of K-means and SOM (unsupervised and supervised) models. Experimental outcomes represented the superiority of SOM models (especially the supervised SOM) over K-means in delineating the geochemical anomaly targets which can be used as an effective and powerful tool for discovering the complex patterns among variables in exploratory geochemical data.

Success rate and learning curve of intraoperative neural monitoring of the external branch of the superior laryngeal nerve in thyroidectomy.

This study aimed to evaluate the success rate and learning curve of intraoperative neural monitoring (IONM) of the external branch of the superior laryngeal nerve (EBSLN) in thyroidectomy. We studied 130 consecutive patients (200 nerves at risk [NARs]) who had undergone conventional thyroidectomy with IONM of the recurrent laryngeal nerve (RLN) and EBSLN from January 2017 to March 2020. The learning curve was defined as the number of NARs required to achieve an effective and reliable neuromonitoring rate. IONM of the EBSLN was successful in 136/200 (68%) NARs. The cutoff point of the learning curve was 45 NARs (28 patients). The neural identification rate of EBSLN was significantly higher in the post-learning 155 NARs group than the pre-learning group (82.6% vs. 17.8%, p < 0.001). Approximately 45 NAR neuromonitoring attempts were required to reach a significant increase in the EBSLN identification rate and overcome the learning curve.

Comparative Analysis on Classification Methods of Geological Disaster Susceptibility Assessment

Objectives Geological hazards not only cause serious economic losses and ecological damage, but also threaten the survival of mankind. The evaluation of geological hazard susceptibility is the basis of risk assessment of geological hazards. Previous studies focused on the selection of susceptibility assessment methods, but less on how to classify the susceptibility index of geological hazards. However, there is no good quantitative classification standard for the susceptibility of geological hazards in the current research results. Methods Taking Wenchuan County of Sichuan Province as an example, twelve widely used factors affecting geological hazard susceptibility was selected, and the susceptibility assessment was carried out by using the information quantity model. The evaluation accuracy of the model was tested by the success rate curve. We proposed a quantitative classification standard for susceptibility. The susceptibility index is a cumulative curve of the proportion of geological hazards in descending order, and the susceptibility index is divided into five intervals: 5% of historical disaster points (low-prone), the remaining 10% (medium-prone), the remaining 20% (high-prone), and the remaining 65% (very-high). Results The method of cumulative proportion subsection of historical geological hazards was compared with other eight methods and the accuracy of classification is verified. The results showed that the evaluation accuracy of the model was checked by two methods of validating the sample success rate curve and the non-disaster point sample success rate curve, and the rationality of the prediction results of the evaluation model was determined. The cumulative proportion subsection method of historical geological hazards showed good reasonableness in three ways: The proportion accuracy verification of vulnerable classification area, the frequency ratio accuracy verification of geological hazards and the location classification accuracy verification of geological hazards. It was the best classification standard in nine classification methods. Conclusions The quantitative classification standard for the susceptibility of geological hazards established in this article has a good application effect, but this standard needs more examples to verify. Geological hazard susceptibility evaluation is based on a good factor classification. Research work needs not only to focus on scientific and advanced evaluation methods, but also basic research on how to select factors and rational classification of factors.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

Comparison of data-driven landslide susceptibility assessment using weight of evidence, information value, frequency ratio and certainly factor methods

Landslide susceptibility assessment is essential for development activities and disaster management in the mountainous regions to identify the landslide-prone areas. The present study aimed to evaluate and compare the efficacy of data driven quantitative models of landslide susceptibility assessment using geospatial tools in Jhelum valley of the Himalayas. This area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. Four probabilistic data-driven models are employed for this purpose, which includes the weight of evidence (WOE), information value method (IVM), frequency ratio (FR), and certainty factor (CF). These assessed models are based on integrating landslide contributing factors and a ground truthing-based landslide inventory of 437 landslides. The landslide susceptibility maps were presented by categorizing the study area into very low to very high susceptibility zone by Jenks natural breaks method. The performance of models was evaluated by a sensitivity analysis using Receiver Operator Curve (ROC) method. The ROC-validated results of success rate curves for WOE, IVM, FR and CF were 80 %, 78 %, 77 %, and 76 % respectively. The prediction rate curve of WOE, IVM, FR, and CF was 78 %, 77 %, 75 %, and 78 % respectively. The results showed the reasonable efficiency of applied models for landslide susceptibility assessment in the study area and applicable to regions with similar geomorphological conditions. Conclusively, the comparison of applied models revealed the promising results of used approaches.

Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria

Landslide is one of the most morpho-dynamic patterns of slopes in the coastal region of Syria. The assessment of landslide susceptibility is a complex procedure because of limited data and lack of related literature in Syria. Therefore, the current paper explored the landslide susceptibility in Al-Fawar basin, based on remote sensing data in the GIS environment. Inventorization of the current landslide was carried out as points utilizing satellite images, fieldwork, and photographs. Thirteen dominant factors of landslide including slope angle, slope aspect, curvature, plan curvature, profile curvature, elevation, proximity to faults, proximity to streams, proximity to roads, NDVI, rainfall, lithology, and TWI were utilized for landslide susceptibility mapping. In this study, two bivariate statistical models, i.e., frequency ratio (FR) and statistical index (SI), were used for spatial calibration between the resulting inventory map and the geofactors. The quality of models performance in mapping the landslide susceptibility was assessed utilizing receiver operating characteristic (ROC) curves. The prediction accuracy curves showed that the landslide susceptibility map using FR model has the greatest prediction accuracy (AUC = 0.824) compared with SI model that showed 0.801 of AUC. Identically, the success rate curve graph showed that the AUC for FR and SI models was 0.799 and 0.778, respectively. Although the susceptibility assessment utilizing the FR model produced higher accurate output, the validation results indicate that the two models used in this study can be encouraging approaches for the assessment of landslide susceptibility hazard in the Syrian coastal basin, thus fruitfully creating spatial mitigation and conservation measures.