Evidential Belief Function Research Articles

Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)

Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.

Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models

Soil degradation induced by gully erosion represents a worldwide problem in the many arid and semi-arid countries, such as Iran. This study assessed: (1) the importance of variables that control gully erosion using the Boruta algorithm, (2) the relationship among causative variables and gullied locations using the evidential belief function model (EBF), and (3) gully erosion development using the algorithms of boosted regression tree (BRT) and support vector machine (SVM). Based on the results of the Boruta algorithm, slope, land use, lithology, plan curvature, and elevation were the most important factors controlling gully erosion. The results of the EBF model showed the predominance of gully erosion on rangeland and loess-marl deposition. The predominance of gullied locations on the concave positions, with the slope of 5°–20° in the vicinity of drainage lines, illustrates a preferential topographic zone and, therefore, a terrain threshold for gullying. The correlation of gullied locations with rangelands and weak soils in concave positions demonstrates that the interactions among soil characteristics, topography, and land use stimulate a low topographic threshold for gullies development. These relationships are consistent with the threshold concept that a given soil, land use, and climate within a given landscape encourage a given drainage area and a critical soil surface slope that are necessary for gully incision. Furthermore, the BRF-SVM had the highest efficiency and the lowest root mean square error, followed by BRT for predicting gully development, compared with LN-SVM algorithm. The application of two machine learning methods for predicting the gully head cut susceptibility in northern Iran showed that the maps generated by these algorithms could provide an appropriate strategy for geo-conservation and restoration efforts in gullying-prone areas.

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

Spatial mapping of groundwater potential using data-driven evidential belief function, knowledge-based analytic hierarchy process and an ensemble approach

Spatial mapping of groundwater potential using data-driven evidential belief function, knowledge-based analytic hierarchy process and an ensemble approach

A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models

In recent years, gully erosion has ceased many development activities and imposed a living threat to local communities residing in southern Iran. Hence, this study sets out to investigate the prediction performance of a machine learning model named the quick, unbiased, efficient statistical tree (QUEST) model for gully susceptibility mapping. Its results were compared to two conventional statistical models: frequency ratio (FR) and evidential belief function (EBF). The area under the receiver operating characteristic (AUROC) and the true skill statistic (TSS) metrics were adopted to assess models' goodness-of-fit and predictive performance in the corresponding training and validation stages. Results revealed that the QUEST model outperforms its counterparts by giving respective AUROC and TSS values of 88.5% and 0.77 in the training stage, followed by EBF (82.3% and 0.65) and FR (80.4% and 0.62). Similarly, the QUEST model showed the highest AUROC and TSS values in the validation stage (83.2% and 0.63, respectively), followed by the EBF (78.6% and 0.63, respectively) and FR (77.1% and 0.58, respectively). Further scrutinization attested that the QUEST model offers a more practical, compendious, and adaptable susceptibility map based on which about 32% of the study area was identified as the high susceptibility zone to gully erosion. Hence, highly gully susceptible areas require pragmatic mitigation plans. In addition, the application of machine learning models for gully erosion merits further studies.

Landslides behavior spatial modeling by using evidential belief function model, Promethean II model, and index of entropy in Tabriz, Iran

Due to the increasing construction of clay and marl hills in most areas of Tabriz (Iran), its characteristics in terms of resistance, and its tendency to liquefy during earthquakes, this city is at risk of landslides. This paper studies the landslide vulnerability of Tabriz using visual and statistical evidence. The evaluation of landslide susceptibility was performed using the evidential belief function model (EBF) and the index of entropy. The environmental impact assessment of landslides was carried out using the Promethean II model in three environmental, economic, and social phases. Finally, a landslide strategy plan for decision-makers was developed. The results of the analysis using the EBF model showed that 89.81% of the total area of Tabriz is located in a direct landslide vulnerability zone. The output of the receiver operating characteristics (ROC) curve showed 83.3% accuracy of the EBF model. The impact assessment showed that for the environment, the geological criterion had an output weight of 0.396; for the economy, the road criterion had a weight of 0.477; and for the society, the commercial criterion had a weight of a 0.452; all were the most affected by landslides. The results of monitoring studies of the largest landslides in Tabriz in 1957, 1984, and 2020 showed approximately 41.65 m of land sliding over a period of 63 years. To our knowledge, this study is the first in the world to predict the environmental impact assessment and provide a strategy plan for Tabriz.

کاربرد مدلهای شبکه عصبی مصنوعی، نسبت فراوانی و تابع شواهد قطعی در تهیه نقشه حساسیت به وقوع سیل در حوزه آبخیز هراز: الگویی برای مطالعات مخاطرات سیلاب شهری

کاربرد مدلهای شبکه عصبی مصنوعی، نسبت فراوانی و تابع شواهد قطعی در تهیه نقشه حساسیت به وقوع سیل در حوزه آبخیز هراز: الگویی برای مطالعات مخاطرات سیلاب شهری

Comparison of statistical and machine learning approaches in land subsidence modelling

This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8).

A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models.

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas.

Flooding and its relationship with land cover change, population growth, and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These disasters are believed to be associated with land use changes and climate variability. However, identifying the factors that lead to flooding is challenging. This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation (BRBP) neural network, classification and regression trees (CART), a statistical model (STM) using the evidence belief function (EBF), and their ensemble models (EMs) for three time periods (2000, 2014, and 2017). The accuracy of machine learning algorithms (MLAs), STM, and EMs were assessed by considering the area under the curve—receiver operating characteristic (AUC-ROC). Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone and other ensemble models for the three time periods analyzed. Furthermore, this study investigated the relationships among land cover change (LCC), population growth (PG), road density (RD), and relative change of flooding (RCF) areas for the period between 2000 and 2017. The results showed that areas with very high susceptibility to flooding increased by 19.72% between 2000 and 2017, while the PG rate increased by 51.68% over the same period. The Pearson correlation coefficient for RCF and RD was calculated to be 0.496. These findings highlight the significant association between floods and causative factors. The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model

Considering the slope units as our reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)]. For this aim, ten conditioning factors correlated with landslide namely, altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, surface roughness, distance to roads, rainfall, and lithology are considered. The performance of the models is tested using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical evaluation measures. The weighted calibrated landslide potential index (WCLPI)-based FR model has the highest predictive capability, followed by the calibrated landslide potential index (CLPI)-based FR, the WCLPI-EBF, the CLPI-EBF, the WCLPI-IOE, the CLPI-IOE, the FR, the EBF, and the IOE models, respectively. Results indicated that hybrid models have improved significantly the performance of single models. This highlights that NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.

An improved approach for predicting the groundwater potentiality in the low desert lands; El-Marashda area, Northwest Qena City, Egypt

The harsh environment of Egypt makes the potentiality assessment of the groundwater of high concern where the groundwater considered as the foremost water supply for irrigation and domestic purposes. In the present work, the frequency ratio (FR), analytical hierarchy process (AHP), and evidential belief function (EBF) models were integrated with Geographic Information System (GIS) and Ternary Image (TI) model as a new integrated approach for evaluating groundwater potentiality. The accuracy of our model was compared applying the area under the curve (AUC). The prediction rates of AUC that acquired for AHP, FR, EBF, and TI were about 72%, 77%, 67% and 79.13%, respectively. Consequently, the Ternary image potential zoning map classified well the Northwest Qena city area depending on the water occurrences. The models validation output demonstrates that both AHP and TI proved an efficiency for groundwater potentials zones. Our new integrated approach can be implemented in the similar harsh areas for water resources potentiality prediction in Africa and other alike areas.

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

Three dimensional mineral prospectivity modeling by evidential belief functions, a case study from Kahang porphyry Cu deposit

The present study has focused on the performance of the evidential belief functions (EBFs) model to predict the three dimensional (3D) mineralization of copper (Cu) at Kahang porphyry Cu deposit (KPCD) in the northeast of Isfahan, Iran. The 3D deposit modeling and reserve estimation are of the most important steps in mineral exploration program. Determining the boundaries of the deposit and estimating the quantity and quality of reserves are among the most essential issues in mining designing. Normally, a 3D model of the sought deposit is prepared based on the mineral grade parameter without considering other parameters and deposit information. In this regard, 3D integration models at the deposit scale facilitate the consideration of other parameters and available underground information. The results of these 3D integration models are mostly used to introduce mineral prospectivity areas. However, in this study, the integrated model was used as an auxiliary parameter in estimation for the geostatistical co-kriging method. Of note is that the accuracy of the 3D model of the deposit improved whereas the estimation error demonstrated a reduction. . The information of 22 boreholes drilled by the National Iranian Copper Industry Company was used in this research. To implement resource modeling, the number of blocks in the eastern, northern and depth directions were 30 × 28 × 145, respectively and the size of each block was designed equal to15 × 15 × 5 m. Auxiliary geospatial models were divided into three main categories of geological, geochemical, and geophysical data sets, and all 3D evidential models were obtained quantitatively. Subsequently, the Dempster-Shafer evidence theory was applied to construct a 3D prospectivity modeling of Kahang deposit. Using this knowledge-driven theory, four 3D models of belief, uncertainty, plausibility, and disbelief were produced, which have substantial impact on more accurate Cu resource modeling. After validation of the prospectivity models with the drilling results, the desired 3D auxiliary model had a correlation of 0.92 with the Cu grades. Thus, the Cu grade was estimated by a co-kriging method using a 3D EBFs model as an auxiliary variable, and output was compared with the one from an ordinary kriging (OK) method. The significance of this study lies in improvement of the performance of the Cu grade modeling through an auxiliary variable, where the root-mean-square error (RMSE) of the estimates reduces from 0.437 to 0.288 when implementing a co-kriging method rather than the ordinary kriging.

Optimization of statistical and machine learning hybrid models for groundwater potential mapping

Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrinehroud watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy (IOE), evidential belief function (EBF) and fuzzy art map (FAM). To accomplish this, 1448 well locations in the study area were randomly divided into two data sets for training (70%= 1013 locations) and validation (30%= 435 locations) based on the holdout method. Fourteen factors that can affect the presence or absence of groundwater were identified, measured, and mapped using ArcGIS and SAGA-GIS software. The models were used to predict the locations of groundwater based on suitable combinations of the conditioning factors to produce groundwater potential maps. The probability of groundwater at any location was classified as low, moderate, high, or very high based on natural breaks in the data spectrum. The model predictions were tested for validity and their success was determined using receiver operating characteristic (ROC) curves, standard errors (SE), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF) and accuracy (ACC), and the Friedman test. The performance assessments of groundwater potential predictions using the area under the curve (AUC) and accuracy (ACC) showed that the FR-RBF model had very good performance (AUC= 0.889, ACC= 87.51). FR-FAM (AUC= 0.869, ACC= 84.67), EBF-FAM (AUC= 0.864, ACC= 84.42), EBF-RBF (AUC= 0.854, ACC= 83.94), FR-IOE (AUC= 0.836, ACC= 83.62), and EBF-IOE (AUC= 0.833, ACC= 80.42) also had acceptable performance. The results of the Friedman test also show that there are significant differences between the models and the highest mean rank was generated by the FR-FAM model (3.642). Therefore, the hybrid models can be used to increase the accuracy of groundwater-prediction models in the study region and perhaps in similar settings. Highlights The groundwater potential was studied in the area of Zarrinehroud watershed A combination of methods including FR, RBF, IOE, EBF and FAM Very high and high groundwater potential areas were located in the northern half The development of hybrid models can increase the accuracy of the results

Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform

Land subsidence causes many problems, including faulting of Earth's surface, damage irrigation systems, reduced soil fertility, misalignment and failure of the walls of wells, changes of river courses and roadways, and even damages in residential areas and agricultural lands. This study assesses groundwater changes using the Google Earth Engine (GEE) and land-subsidence susceptibility (LSS) on the Gharabolagh Plain in Fars Province, Iran, using two probabilistic models, evidential belief function (EBF) and Bayesian theory (BT). In extensive field surveys, the land-subsidence points identified in the study area were recorded by handheld GPS. A land-subsidence location map (LSIM) was prepared in ArcGIS 10.6.1. The sites were divided into two sets: 70% of the subsidence points locations to be used for modeling (calibration) and 30% to be used for testing (validation) of the models. Effective factors that promote land-subsidence occurrence (LSO) were selected and maps for each were prepared from several sources—topographical and geological maps, and satellite images. Changing groundwater level was determined from the GEE platform using GRACE satellite images from a 14-year period. Three methods—CSR (Center for Space Research at the University of Texas), JPL (Jet Propulsion Laboratory), and GFZ (German Research Centre for Geosciences)—were employed and show that the greatest changes in water depth in the study area are equal to − 16.55, − 17.77, and − 15.09 cm from CSR, GFZ, and JPL, respectively. The spatial relationships between land subsidence and the ten effective factors were assessed using the EBF algorithm and BT. Also, analysis of variables importance was done using the random forest model. Prioritizing these factors, it was revealed that excessive withdrawal of groundwater, elevation, and distance from rivers were the most important variables promoting subsidence. Finally, the LSS maps were prepared in GIS. The validation analysis of the two probability models by ROC curve revealed that EBF had 93.2% accuracy and BT was 99.8% accurate. The maps generated with both models can effectively indicate LSS for managers of groundwater and can be used to prevent fatalities, financial losses, and damage to property caused by this phenomenon.

Proposing artificial intelligence based livelihood vulnerability index in river islands

River island (locally charland) of Ganges river from Rajmahal hill to Farakka barrage in India is the human occupancy of the river bank erosion evicted people, but these areas are under different vulnerabilities like bank erosion, flooding, poor livelihood etc. No advanced models are applied yet for depicting livelihood vulnerability state (LVS). However, different advanced machine learning (ML) models successfully applied for predicting different physical vulnerability. The present work intended to apply advanced ML models for precise delineation of LVS and in aim to improve the likelihood of accuracy of the ML models, the study also proposed a new feature selection and evidential belief function based hybrid ensemble machine learning model. Results clearly identified that the proposed model predicted the LVS more accurately than other ML and ML ensemble models like Generalized additive model, bagging, dagging, random subspace and logitboost. Area under curve (AUC) in Receiver operating characteristics (RoC) reported that the accuracy level is 0.96 in case of proposed model over maximum 0.92 predicted by the others. Proposed model accounted that about 36% to the total study area is under high to very high LVS and this area mainly covers the peripheral part of the charland. However, the present study concluded that for delineating the vulnerability state of any area, hybrid ensemble model can be a better option over others.

Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India

The landslide is a downward movement of soil and rock, and one of the most destructive geo-hazards that causes losses in lives, environment, and economy all over the world. Landslide susceptibility mapping is a scientific method to evaluate the landslide probability zones and causative factors. The main objective of the present study was to introduce ensemble landslide susceptibility models which are developed on the basis of two statistical models (evidential belief function and geographically weighted regression) and one machine learning model (random forest) for spatial prediction of landslide of the Upper Rangit River Basin, Sikkim, India. Totally, 102 landslide locations have been identified and randomly classified into 70% and 30% as training and validating database, respectively. Total 16 landslide causative factors are considered and grouped into four categories: geomorphological, hydrological, geological, and environmental factors. The evidential belief function (EBF), geographically weighted regression (GWR), and random forest (RF) method and their ensemble methods, RF-EBF, and RF-GWR models have been applied with the help of training landslide and non-landslide dataset and spatial database of landslide causative factors. Five landslide susceptibility maps have been generated by the said model, and the maps have been validated by validating dataset with the help of sensitivity, specificity, accuracy, Kappa index, and area under curve (AUC) of receiver operating characteristic (ROC) tools. The ensemble methods have the best degree-of-fit and prediction performance than single methods, i.e., RF-EBF and RF-GWR model have 91.8% and 89.9% prediction capabilities. The result of the relative importance of factor showed that land use land cover (LULC), distance to river, soil, drainage density, and road density factors have played the key role in the occurrence of the landslide. The result of the study can be used by local planning, dicession makers, and the methods of landslide susceptibility can be applied in other areas.

Urban Inundation Hazard Potential using Evidential Belief Function model (EBF) (Case study: Emam Ali town, Mashhad city)

Urban Inundation Hazard Potential using Evidential Belief Function model (EBF) (Case study: Emam Ali town, Mashhad city)