Weight Of Evidence Model Research Articles

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluation of landslide susceptibility models and maps. The landslide data of 91 locations were randomly divided into two parts in the ratio of 70 : 30 using GIS software that is 70% data was used for training the models and 30% data was used for testing and validating the models. Performance of the applied models was evaluated using area under the AUC (area under the curve) ROC (receiver operating characteristics) curve. Results indicated that the WOE model is having better accuracy (AUCWOE = 68.75%) than the SE model (AUCSE = 52.17%) in the development of landslide susceptibility maps. Hence, WOE model can be used for the development of accurate landslide susceptibility maps which can provide useful information to decision maker and policy planner in better development of landslide prone areas.

A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods

Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.

Analysis and mapping of mining subsidence and underground voids collapse risk using GIS and the weights-of-evidence model for the abandoned Ichmoul mine, Algeria

The present study concerns the assessment of mining subsidence and old underground works collapse hazard of the abandoned Ichmoul mine (Batna, Algeria) using the geographic information system (GIS) and the weight of evidence method (WofE). After the identification of collapse cases on the site and developing a spatial database for old underground works, topography and geology, the weight of evidence model was applied to calculate the weight of each relevant factor. Five main factors controlling or related to subsidence and underground voids collapse were determined from the probability analysis of existing cases; the underground voids depth, the distance between the underground voids, the landforms slope, the lithology and the distance between the fractures. Conditional independence tests were performed for the choice of factors. For the analysis of the subsidence and underground void collapse spatial hazard mapping, the contrast values, W+ and W- of the each factor evaluation were analyzed. The analysis results were validated using the curve ROC (AUC) with a past collapse case. For all the factors used, the area under the (ROC) curve showed 92 % accuracy. The results obtained can be used for the prevention of mining subsidence and underground voids collapse risks and the mine site rehabilitation.

Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the Indian Himalaya cause considerable damage, posing a serious risk to life and property. This study attributes information regarding landslide triggering parameters to further delineate landslide susceptibility maps of Himachal Pradesh in the Himalayan region. The landslide inventory map was created using information from field visits, Linear Imaging Self-Scanning Sensor (LISS III), and Google Earth. Thereafter, eight landslide causative factors, viz. slope, aspect, curvature, elevation, Landuse Landcover (LULC), soil, lithology, and drainage density were performed by employing the weight of evidence (WOE), information value method (IVM) and the frequency ratio (FR) methods. Using the ArcGIS reclassification tool, the final landslide susceptibility zonation (LSZ) maps were categorized into five susceptibility zones: “very low, low, medium, high, and very high.” The success rate for WOE, FR, and IVM models was determined as 76.27%, 78.20%, and 76.09% respectively, depicting that the FR model based LSZ map is more accurate. According to the FR model based LSZ map, the highly susceptible classes in the case of aspect, curvature, and lithology are southeast, concave, and TBS, respectively. The sparsely vegetated areas are more prone to landslides than the other LULC areas. The higher elevations, ranging from 1191 to 1434, 1434–1655, and 1655–1876 m, are more vulnerable to landslide activities as compared to low elevations. The slope classes 30–45 and 45–60, medium drainage density class and fine loamy class of soils are more likely to landslides. The prepared susceptibility zone map can be used for future mitigation planning in the high and very high susceptibility zones in order to reduce landslide-related human and economic losses.

A spatial analysis for geothermal energy exploration using bivariate predictive modelling

The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.

A comparative study of different statistical methods for Flood susceptibility assessment: A case study of N'fis basin, Marrakesh High Atlas (Morocco)

Floods are one of the natural disasters with farreaching socio-economic and environmental consequences. A large part of the N'fis catchment area is vulnerable to the risk of flooding which causes an immense loss of people and infrastructure. Therefore, an accurate assessment for the susceptibility of natural risks remains essential. The main objective of this study is to evaluate the performance of frequency ratio (FR), informative value (IV) and weight of evidence (WoE) models in flood sensitivity mapping in the N'fis watershed, high atlas of Marrakech in central Morocco. A total of 87 paleo-floods sites were inventoried based on data from Tensift hydraulic agency and extensive field surveys and 11 causative factors were considered in this study. The results show high susceptibility in the northern part of the N'fis basin and moderate to low susceptibility in its southern parts. The maps were validated according to the receiver operating characteristic (ROC) curve and the area under the curves (AUC) was calculated. Then, the accuracy rates are of the order of 85.65%, 87.75% and 88.40% for the FR, IV and WoE models respectively. Thus, the WoE model proved to be the most significative model for the analysis of flood sensitivity in this region. The results of this work can be an important support for decisionmakers for flood risk-appropriate planning.

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.

Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan

The primary objective of this study is to analyze and characterize landslides in North Pakistan along Karakoram Highway (KKH) to produce a landslide susceptibility map using GIS and remote sensing technology. Using satellite images followed by field investigations, spatial distribution of landslide database was generated. Next, an integrated study was undertaken in the study area to perform the landslide susceptibility mapping. Dubaur-Dudishal section of KKH (about 150 km) which is a part of Kohistan Island Arc, is investigated in this study with a buffer zone of about 8 km along both sides of the KKH. Several thematic maps, e.g., lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI), slope, aspect, elevation, relative relief, plan-curvature and profile-curvature were prepared. Subsequently, these thematic data layers were analyzed by frequency ratio (FR) model and weights-of-evidence (WoE) model to generate the landslide susceptibility maps. In order to check the accuracy of the models, the area under the curve (AUC) was to quantitatively compare the two models used in this study. The predictive ability of AUC values indicate that the success rates of FR model and WoE model are 0.807 and 0.866, whereas the prediction rates are 0.785 and 0.846, respectively. Both methods show that nearly 50 % landslides in the study area fall in either high or very high susceptibility zones. The landslide susceptibility maps presented in this study are of great importance to the policy makers and the engineers for highway construction as well as the mega dams construction projects (Dasu dam and Bhasha dam which lie within the vicinity of the study area); so that proper prevention as well as mitigation could be done in advance to avoid the possible economic as well as the human loss in future.

Simulation of water hyacinth growth area based on multi-source geographic information data: An integrated method of WOE and AHP

Predicting the suitable growth areas (SGAs) of WH under different environmental scenarios are important for sustainable management of water hyacinth (WH) which is more severe than ever due to increased water pollution caused by human activities. Based on an integrated method of Weight-of-Evidence (WOE) and Analytical Hierarchy Process (AHP), this study developed habitat adaptability index (HAI) to predict the SGA under four environmental scenarios using 412 WH events and 15 environmental variables in Guangdong of South China, which has significantly meteorological and hydrological characteristics of annual average (AAS), April to September (high flow season, HFS), October to March (low flow season, LFS), and December or next January (the coldest month, CMS). In each scenario, multi-classified environmental variables (i.e., topography, meteorology, socioeconomics, land cover, and urban sewage treatment) of WH events were firstly weighted by the WOE model. Then, the WOE weights were further input to AHP as data matrix for calculating the final weights which were used to analyze the impact of environmental variables on WH growth. The integrated WOE-AHP method under four scenarios performs well with the area under the curve (AUC > 0.85) of Receiver Operating Characteristic (ROC). Under different scenarios, the risk simulation of WH disaster shows the following distribution law: HFS > LFS > AAS > CMS. More than 54% of water areas in the study area is suitable for the growth of WH. WH density increased from north to south (or upstream to downstream) along rivers while the density decreased from central to both east and west wings of Guangdong province. The most WH-prone occurrence regions are located at elevation < 50 m, slope < 2°, gross industrial production > RMB 10, 000 million yuan, people > 5 million persons, air temperature between 25.4 °C and 27.7 °C, precipitation between 1575 mm and 1864 mm, and annual average evapotranspiration between 904 mm and 1,065 mm. The Pearl River Delta, Zhanjiang-Maoming regions of western Guangdong, and Teochew area of Eastern Guangdong are three major risk areas. Under the background of global warming and serious deterioration of the aquatic environment, the SGA of WH is expanding northward. Based on the location of the water system in Guangdong province, we proposed two WH management strategies for rural and urban water systems. Given macroscopic and rapid advantages of WOE-AHP in simulating WH’s SGA, this method can be applied to other areas invaded by WH, and the scenario setting and selection criteria of environmental factors can provide important reference for adopting targeted WH management measures.

Delineation of groundwater potentials of Sfax region, Tunisia, using fuzzy analytical hierarchy process, frequency ratio, and weights of evidence models

Groundwater in semiarid regions is of extreme importance due to limited water resources and increasing population demand. Hence, a better knowledge of aquifer potentialities is required for better management of this precious resource. This study aims to assess the groundwater potential map (GPM) by both statistical methods and geographical information system (GIS) in Sfax region, Tunisia. A number of 11,868 wells in the region were mapped in GIS and divided into two data sets: 8308 wells (70%) were selected in a random way and defined as training, wells while the remaining ones (3560 as 30%) were considered as testing wells for model validation. First, the groundwater conditioning factors, namely altitude, slope, lithology, drainage density, lineament density and land use maps, were evaluated and then processed by the fuzzy analytical hierarchy process (FAHP), frequency ratio (FR), and weights of evidence (WOE) statistical models. Then, the groundwater potential index (GWPI) was generated from an overlap of the weighted and rated of all the conditioning factors for the three methods. The groundwater potential maps were produced in ArcGIS 10.3 for the three models and classified by means of the quantile classification method. As a final step, the receiver operating characteristics (ROC) curves validated these maps. The validation results show from the areas under the curves (AUC) that the WOE model (AUC = 71.4%) performs similarly to the FR model (AUC = 71.1%), and both are slightly better than the FAHP (AUC = 65.1%) model. Thus, the delineation of groundwater potential zones supports the decision makers for the management of the aquifer exploitation. The appropriate groundwater potential map is established for a suitable management and a better planning of the water resources. Preventive works in low potential coastal areas should be conducted in the present and considered for a long-term management. Given the close results of the three adopted methods, it is possible to apply them in other regions with a consideration of their specific characteristics.

Developmental Stuttering Screening Instrument: evidence of sensitivity and accuracy measures

ABSTRACT Purpose: to verify the sensitivity and accuracy measures of the Developmental Stuttering Screening Instrument (DSSI). Methods: the DSSI was administered to 30 parents/guardians of children aged 2 to 5 years and 11 months with and without complaint of stuttering. The instrument administration was timed. The sensitivity analysis used the Weight of Evidence (WoE) binary classification model to verify the strength level of the items. The cutoff scores were established with grouping analysis with the k-means cluster method, based on the minimum and maximum values of each identified group’s scores. The data were analyzed with the SPSS statistical software (version 20.0) and were considered significant with p ≤ 0.05. Results: the interviews lasted an overall mean of 17 minutes. The WoE model revealed that the items with the greatest predictive strength for risk of stuttering were the social reaction to their speech, the physical concomitants, and the comprehension of the child’s speech. The correspondence analysis showed a strong association between “having complaints” and “high total score”, as well as between “not having complaints” and “low total score”, indicating that the parents’ complaints are a factor that leads to high scores in the instrument. “Sex” had little predictive effect for risk. The grouping analysis enabled the stratification of subjects into three risk levels: “not at risk”, “under observation”, and “at risk”. Conclusion: the instrument presented the first evidence of sensitivity and accuracy measures, thus, making the identification of risk of developmental stuttering in preschoolers, possible.

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.

3DWofE: An open-source software package for three-dimensional weights of evidence modeling

Abstract The shortage of outcropping ore deposits and the inefficiency of traditional mineral exploration methods for discovering deep-seated deposits have increased the necessity of developing three-dimensional modeling methods for in-depth exploration. The 3DWofE is a Python-based open-source software package that provides the tools required for three-dimensional modeling of concealed ore bodies by developing a data-driven prospectivity modeling method called weights of evidence. This software enables users to create three types of models which include posterior probability, uncertainty, and studentized posterior probability. These models can guide exploration geologists through identifying potential regions of target mineralization in depth.

Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)

Landslide susceptibility assessment is an important task in urban planning and risk management. For mountainous areas where multiple types of landslides occur, the differences between landslide types should be considered when mapping landslide susceptibility. The main objective of the present study is to compare the prediction performances of three statistically-based models, namely, information value (IV), logistic regression (LR), and weight of evidence (WOE) models, in susceptibility assessments of different landslide types and to optimize the combination of causal factors. The Loess Plateau area in Shanxi Province, China, was selected as the study area. We prepared a landslide inventory by mapping the detailed spatial distribution of 234 loess landslides and 232 rockfalls in the area. Subsequently, 11 causal factors were considered in the construction of an initial index system for the susceptibility assessments, and corresponding thematic layers were generated in a GIS environment. Principal component analysis was utilized to obtain the relative importance of these layers in causing loess landslides and rockfalls, and nine factors with relatively high weights were selected as the final landslide influencing parameters. The relationships between these parameters and landslide distribution were analyzed using the three aforementioned methods and training samples (75% of landslides). The outcomes were extended to the entire study area to determine landslide susceptibility, and the testing samples (25% of landslides) were used to verify the predictive ability of the models. The results illustrated that rainfall and land use were essential in predicting both loess landslide and rockfall occurrences. The WOE and LR models showed the best performance in rockfall and loess landslide susceptibility assessments, respectively. Both models had an accuracy of more than 80%. If both landslide types were considered as an entire group or if all 11 factors were included in the model, the accuracy decreased.

GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia

Simada area is found in the South Gondar Zone of Amhara National Regional State and it is 780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of Ethiopia. This area is part of the Guna Mountain which is characterized by weathered volcanic rocks, rugged morphology with deeply incised gorges, heavy rainfall and active surface processes. Many landslides have occurred on August 2018 after a period of heavy rainfall and they caused many damages to the local people. In this study, Frequency Ratio (FR) and Weights of Evidence (WoE) models were applied to evaluate the landslide causative factors and generate landslide susceptibility maps (LSMs). The landslide inventory map that consists of 576 active and passive landslide scarps was prepared from intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors including aspect, slope, curvature, lithology, land use, rainfall and distance to stream were combined with a training dataset using GIS tools to generate the LSMs of the study area. Then the area was divided into five landslide susceptibility zones of very low, low, moderate, high and very high. Later, the resulting maps have been validated by using area under the curve and landslide density index methods. The result showed that the predictive rate of FR and WoE models were 88.2% and 84.8%, respectively. This indicated that the LSM produced by FR model showed a better performance than that of WoE model. Finally, the LSMs produced by FR and WoE models can be used by decision-makers for land use planning and landslide mitigation purpose.

Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India

The main objectives of the current study is to generate and compare the landslide susceptibility maps (LSMs) using a probabilistic model i.e. weight of evidence (WoE), a machine learning technique i.e. support vector machine (SVM) and ensemble approach i.e. WoE-SVM. Experiments were conducted in the Kurseong region, a part of Darjeeling Himalaya as a study area. Previous landslide sites were identified through field survey and previous records. Total 273 landslide sites were compiled and considered for the landslide inventory map to calibrate as well as validate the models. Seventeen landslide conditioning factors (LCFs) were used for landslide susceptibility modelling, i.e. slope degree, elevation, aspect, curvature, rainfall, geological structure, lineament, distance to main roads, soil texture, soil depth, earthquake zone, land-use/land-cover, drainage density, stream power index, sediment transport index, and topographic wetness index. The generated landslide susceptibility maps were validated applying the receiver operating characteristic curves (ROC), Positive Predictive Value (PPV), Proportion Correctly Classified (PCC) and the seed cell area index (SCAI). The results revealed that prediction rates are 89.47%, 91.81%, and 94.45% with standard error 0.0359, 0.0289 and 0.0183 respectively for WoE, SVM and ensemble WoE-SVM models. The study shows that the fellow land, plantation area, roads side areas and denudation structural hills having elevation more than 1500 m and 24° to 69° slopes are extremely susceptible to landslides. The comparison of the produced landslide maps revealed that all the applicable models have committed accuracy for studying sensitivity in Kurseong region of Darjeeling Himalaya. The ensemble model has better capability than the other two models. The results may be helpful for landslide risk mitigation in the Kurseong and its surrounding region having similar terrain region and geological conditions.

Landslide Vulnerability Zone by Weights of Evidence Model using Remote Sensing and GIS, in Kodaikanal Taluk (Tamil nadu, India)

Landslide Vulnerability Zone by Weights of Evidence Model using Remote Sensing and GIS, in Kodaikanal Taluk (Tamil nadu, India)

Spatio-statistical comparative approaches for landslide susceptibility modeling: case of Mae Phun, Uttaradit Province, Thailand

In this study for landslide susceptibility modeling, three quantitative techniques, i.e., frequency ratio (FR), information value (IFV), and weight of evidence (WOE), were evaluated and compared. For this purpose, landslide inventory map was prepared using visual interpretation on SPOT-5 image and field survey was carried out for ground truthing of landslide sites and total 677 landslides were identified. The inventory map was divided into training and validation datasets, and from total, 473 landslides (70%) were for training to run the model and 30% (204 landslides) for validation purpose. Total 11 landslide conditioning factors were used in this study that are: elevation, slope, aspect, curvature, plan curvature, profile curvature, land use/land cover (LULC), topographic wetness index (TWI), stream power index (SPI), proximity to road, and proximity to stream. Three different landslide susceptibility maps were produced based on analyzing the relationship of landslides with conditioning factors using FR, IFV, and WOE in GIS environment. The results of FR model indicated that almost 40% of the total study area fall in high to very high landslide susceptibility zones, while in WOE and IFV models, it was found almost 50% of the total area. The landslide susceptibility maps were validated using prediction and success rate curve techniques. The prediction rate curve gives us a glimpse of future landslides based on present landslide susceptibility maps. The results obtained from validation showed that the area under curve (AUC) based on prediction rate curve for FR, IFV, and WOE was 80.78%, 72.88%, and 72.33%, respectively. However, the AUC obtained through success rate curve for the models in this study was 74.60%, 75.04%, and 72.54% for FR, IFV, and WOE, respectively. Moreover, the evaluation of landslide density test and seed cell index area (SCAI) indicated that calculated and classified landslide susceptibility maps are in a good agreement with the field conditions. Thus, it was observed from this study that the frequency ratio has better accuracy as compared to information value and weight of evidence, but in success rate curve, almost all the models showed the same results. Consequently, it can be concluded that the susceptibility maps produced from FR, IFV, and WOE are in good agreement because more than two-third of landslides falls in high and very high susceptibility zones of each model. From this study, it was found that slope angle, elevation, land use/land cover, and roads play a major influencing role in the occurrence of landslide in the study area. The maps produced based on these landslide susceptibility models provide a base for engineers and land use planner to develop landslide management strategies before any development on slopes.

Leveraging complementary computational models for prioritizing chemicals of developmental and reproductive toxicity concern: an example of food contact materials

The evaluation of developmental and reproductive toxicity of food contact materials (FCMs) is an important task for food safety. Since traditional experiments are both time-consuming and labor-intensive, only a small number of FCMs have sufficient toxicological data for evaluating their effects on human health. While computational methods such as structural alerts and quantitative structure-activity relationships can serve as first-line tools for the identification of chemicals of high toxicity concern, models with binary outputs and unsatisfied accuracy and coverage prevent the use of computational methods for prioritizing chemicals of high concern. This study proposed a genetic algorithm-based method to develop a weight-of-evidence (WoE) model leveraging complementary methods of structural alerts, quantitative structure-activity relationships and in silico toxicogenomics models for chemical prioritization. The WoE model was applied to evaluate 623 food contact chemicals and identify 26 chemicals of high toxicity concern, where 13 chemicals have been reported to be developmental or reproductive toxic and further experiments are suggested for the remaining 13 chemicals without toxicity data related to developmental and reproductive effects. The proposed WoE model is potentially useful for prioritizing chemicals of high toxicity concern and the methodology may be applied to toxicities other than developmental and reproductive toxicity.