Weights Of Evidence Method Research Articles

Prediction and evaluation of concealed ore body in 3D environment

3D models of stratigraphy、 rock、 tectonic、 mineralization and so on are established, based on the data of digital uranium exploration achievement, focusing on 3D modeling of uranium deposit、 evaluation of deep uranium resource. Exploration achievement model are formed by the result above accompany with geophysical geochemical prospecting data and Interpretation. The prediction and evaluation of deep deposits which delineated metallogenic advantageous target area are carried out by weights of evidence method. The applications of digital uranium exploration achievement are realized by the application research above, which improving efficiency and broadening application range and depth of digital exploration achievement.

Three-Dimensional Prospectivity Modeling of Honghai Volcanogenic Massive Sulfide Cu–Zn Deposit, Eastern Tianshan, Northwestern China Using Weights of Evidence and Fuzzy Logic

Three-dimensional (3D) prospectivity modeling based on the weights of evidence and fuzzy logic methods was carried out for the Honghai volcanogenic massive sulfide Cu–Zn deposit in eastern Tianshan, northwestern China. A 3D geological model was constructed using geological maps, geological plans, cross sections, and boreholes. The geological model and metallogenic model of the Honghai deposit were used to generate 3D predictor maps. The weights of evidence method and fuzzy logic were then used to integrate the various predictor maps to create prospectivity maps. Capture efficiency curves were subsequently used to delineate high-prospectivity areas in the prospectivity maps. The weights of evidence method and fuzzy logic delineated 96.13% and 90.60%, respectively, of the known mineralization in the high-prospectivity areas, which occupied about 5.89% and 6.33% of the study area. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the two methods, with both showing area under the curve values > 0.5, which indicates the effectiveness of both methods for 3D prospectivity modeling of the Honghai deposit. However, the weights of evidence method generally performed better than fuzzy logic for identification of the concealed and deep-seated Honghai deposit. Conversely, fuzzy logic exhibited better generalization capability. Based on the findings of this study, the high-prospectivity areas located in the south of the known orebody of the Honghai deposit should be considered as high-priority targets for future mineral exploration. This study aims to enable more effective delineation of concealed and deep-seated exploration targets in the Honghai deposit.

Using data linkage to enhance the reporting of cancer outcomes of\xa0Aboriginal and Torres Strait Islander people in NSW, Australia

BackgroundAboriginal people are known to be under-recorded in routinely collected datasets in Australia. This study examined methods for enhancing the reporting of cancer incidence among Aboriginal people using linked data methodologies.MethodsInvasive cancers diagnosed in New South Wales (NSW), Australia, in 2010–2014 were identified from the NSW Cancer Registry (NSWCR). The NSWCR data were linked to the NSW Admitted Patient Data Collection, the NSW Emergency Department Data Collection and the Australian Coordinating Register Cause of Death Unit Record File. The following methods for enhancing the identification of Aboriginal people were used: ‘ever-reported’, ‘reported on most recent record’, ‘weight of evidence’ and ‘multi-stage median’. The impact of these methods on the number of cancer cases and age-standardised cancer incidence rates (ASR) among Aboriginal people was explored.ResultsOf the 204,948 cases of invasive cancer, 2703 (1.3%) were recorded as Aboriginal on the NSWCR. This increased with enhancement methods to 4184 (2.0%, ‘ever’), 3257 (1.6%, ‘most recent’), 3580 (1.7%, ‘weight of evidence’) and 3583 (1.7%, ‘multi-stage median’). Enhancement was generally greater in relative terms for males, people aged 25–34 years, people with cancers of localised or unknown degree of spread, people living in urban areas and areas with less socio-economic disadvantage. All enhancement methods increased ASRs for Aboriginal people. The weight of evidence method increased the overall ASR by 42% for males (894.1 per 100,000, 95% CI 844.5–945.4) and 27% for females (642.7 per 100,000, 95% CI 607.9–678.7). Greatest relative increases were observed for melanoma and prostate cancer incidence (126 and 63%, respectively). ASRs for prostate and breast cancer increased from below to above the ASRs of non-Aboriginal people with enhancement of Aboriginal status.ConclusionsAll data linkage methods increased the number of cancer cases and ASRs for Aboriginal people. Enhancement varied by demographic and cancer characteristics. We considered the weight of evidence method to be most suitable for population-level reporting of cancer incidence among Aboriginal people. The impact of enhancement on disparities in cancer outcomes between Aboriginal and non-Aboriginal people should be further examined.

SPATIAL PREDICTION MODELS FOR LANDSLIDE ACTIVITY MAPPING USING VEGETATION ANOMALIES

Abstract. An area that located in Kundasang which in Ranau district in Sabah, Malaysia that lies along the bank of Kundasang valley was chosen for comparing the reliability of frequency ratio (FR) and weight of evidence (WoE) methods for landslide activity probability mapping by using related vegetation anomalies indicator. The locations of 47 and 189 of active and dormant landslides respectively were identified using 4 raster layers (topographic openness, hillshade, colour composite and high resolution orthophoto). Each landslide activites were randomly divided into two groups as training (70%) and testing (30%) datasets. Tree height irregularities, DVI, NDVI, SAVI, and OSAVI were considered as landslide bio-indicator. The landslide activity probability maps were prepared using the FR and WoE method. The generated maps were validated by calculating the success and prediction rates from area under receiver operating characteristics (ROC) curve. The results of WoE method were relatively reliable (AUC > 0.8) for dormant landslide while only about 40% of active landslide have been predicted accurately. Similar trend yielded for FR method where least accuracy for active landslide prediction.

Comparison of 3D prospectivity modeling methods for Fe-Cu skarn deposits: A case study of the Zhuchong Fe-Cu deposit in the Yueshan orefield (Anhui), eastern China

Three-dimensional (3D) metallogenic prospectivity modeling has become increasingly important in deep Fe-Cu skarn mineralization prospecting. The selection of prediction models and their comparison are of great significance. In this paper, we integrated the geological and geophysical data from the Zhuchong Fe-Cu deposit in the Yueshan orefield (Anhui, eastern China) to establish the local 3D geological framework. We extracted the 3D ore-controlling factors quantitatively by means of 3D spatial analysis methods, and compared the 3D mineralization locations predicted by the weight of evidence method and the artificial neural network method. The results show that both methods can locate most of the known orebodies, suggesting that both are relatively reliable. In addition, the weight of evidence method performed distinctly better than the artificial neural network method in terms of both prediction effectiveness and efficiency. Therefore, the weight of evidence method may be more applicable in 3D prospectivity modelling for Fe-Cu skarn deposits in the region. Finally, the composite area predicted by the two methods is taken as the final prediction result of the mineralization area of the deposit. And it is suggested that in the quantitative 3D prediction, multiple selection of different methods with good adaptability should be involved in the prediction at the same time, and compound prediction method should be used to delineate the target area. Our work has guiding significance for future deep prospecting and exploitation for other similar Fe-Cu skarn deposits.

MODELLING OF POPULATION EXPOSURE TO LANDSLIDE RISK IN SUKABUMI, INDONESIA USING GIS

Abstract. Modelling population exposure to landslide risk is essential for mitigating the damage of landslides. This research aims to assess population exposure to the modelled landslide risk in the Sukabumi region, Indonesia. Also assessed in this study is the importance of 10 environmental variables and their spatial association with past landslide occurrence using the Weight of Evidence (WOE) method. The accuracy of the modelled landslide susceptibility is assessed using the AUC ROC method. Village level population was spatially redistributed via dasymetric modelling, and overlaid with the modelled landslide susceptibility map differentiated by the source zone and the runout zone. It is found that slope, curvature, and soil are the three most influential variables of landslides. The WOE method is able to achieve a similar success rate (0.877) and prediction rate (0.876) in modelling landslide susceptibility. In 2017, medium (114,588 ha) and high (106,337 ha) susceptibility levels were the two largest classes while low (94,778 ha), very high (52,560), and very low (51,910 ha) susceptibility classes are much less extensive. An absolute majority of the population faces a high (1,081,875 people or 38.98% of the total population), and a medium (1,036,080 people or 37.33%) level of landslide risk. Those facing a low (409,658 people or 14.76%), very high (168,193 people or 6.06%), and very low susceptibility (79,656 people or 2.87%) account for slightly more than one fifth of the total population. These findings demonstrate the critical role of GIS in assessing the exposure of population to landslide risk from a diverse range of variables.

Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

Landslide susceptibility mapping of the Tehri reservoir rim area using the weights of evidence method

This study was aimed to utilise important landslide causal factors for the delineation of the landslide susceptible area using the weights of evidence (WofE) method in the Tehri reservoir rim region on a macro scale. The Tehri reservoir extends up to 70 km and bounded by moderate to steep slopes. Landslide susceptibility mapping (LSM) is an essential measure for identifying the potentially unstable slopes bounding the reservoir. With the help of ancillary data, remote sensing imagery and a digital elevation model, 10 causative factors along with landslide inventory were extracted. Initially, the WofE model was applied to obtain the association between landslides and causative factors. The process gave the numerical estimate of correlation between landslides and causative factors by means of positive and negative correlation. Important factor attributes, potentially causing landslides, were identified based on high positive correlation values. Later, the posterior probability of landslide occurrence for each mapping unit was also computed using the WofE model. Posterior probability was divided into five relative susceptibility classes. Validation of the posterior probability map was carried out by using the prediction rate curve technique and a reasonable accuracy of 83% was achieved. LSM of the Tehri reservoir rim area implicates unplanned road construction and settlements coupled with the reservoir slope settlement process for the present degradation of the geo-environmental system in that region.

Relationship between Groundwater Resources and Quaternary Faults of Sirjan Plain Using Weight of Evidence Method

Relationship between Groundwater Resources and Quaternary Faults of Sirjan Plain Using Weight of Evidence Method

Mapping of groundwater spring potential zone using geospatial techniques in the Central Nepal Himalayas: A case example of Melamchi–Larke area

Studies assessing the groundwater spring potential in the Himalayan mountain slopes are very important for sustainable water resources management and build climate resilience in mountains, but such studies are few in the Himalayas. Hence, this paper attempts to identify the groundwater spring potential zone in the Central Himalayas of Nepal. About 412 groundwater springs were surveyed, which were mainly originated from the weathered, jointed or fractured rock aquifers in the high-grade metamorphosed rocks. Eleven influencing factors, viz., altitude, slope gradient, slope shape, relative relief, flow accumulation, drainage density, geology, lineament density, land use and vegetation density were considered in assessing the groundwater spring potential using the weight of evidence method. Weight indicating the probability of groundwater spring occurrence on multiple classes of each factor was calculated and finally summed up to determine the groundwater spring potential. Gentle slope, low relative relief, high flow accumulation, north- and east-facing slopes, denser lineament density, altitude class of 1500–2500 m, high vegetation density, and forest demonstrated a higher likelihood of spring occurrence. Validation of the groundwater spring potential map was successful, which implies the method can be replicated in a similar biophysical environment, where the hydrogeological or geophysical surveyed data is not available.

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

MODELING SPATIAL DYNAMICS APPLIED TO THE ASSESSMENT OF URBAN GROWTH IN THE VICINITY OF PIPELINES IN RIO DE JANEIRO

The use of mathematical models in environmental studies allows significant scientific contributions to the physical planning of an area, since they help to understand the impact of changes in land cover and predict future trends of changes in ecosystems. The study area includes portions of the municipalities of Duque de Caxias, Nova Iguaçu, and Belford Roxo, located in the state of Rio de Janeiro, Brazil, and aims to carry out experiments in land cover changes in the medium-term (10 years) within the catchment area of ORBEL pipelines. The modeling performed in this study was developed using the software Dinamica EGO. For model calibration, we used the weights of evidence method. Positive values foster certain transitions, while negative values indicate low probability of transition. Model validation was executed by means of the fuzzy similarity method using exponential decay. The results show that for the study period (1998-2010) the rate of change for the transition of the class 'others to woody vegetation' is much higher than for the other class transitions observed in the same periods. One annual land cover scenario was generated for the year 2020. The scenario indicated the growth of suburbs near the ring road around Rio de Janeiro, especially in the municipality of Duque de Caxias.

Identification of Potential Landslide Susceptible Area in the Lesser Himalayan Terrain of Nepal

Landslide susceptibility mapping is a very important tool to identify potential landslide-prone areas. In this work, weight of evidence method is applied to obtain landslide susceptibility assessment. Weight of evidence model is commonly applied in the landslide study as it is widely acceptable and easy to use. The objective of this paper is to prepare the landslide susceptibility map of Lung Khola catchment, Pyuthan District of Nepal. Altogether, 84 landslides were identified after landslide inventory. The thematic layers of all causative factors and existing landslides are prepared in Arc GISsoftware. Mainly, Digital Elevation Model (DEM) based causative factors and field data were used to prepare the data layers of the causative factors. In this research, 8 intrinsic factors were used for the landslide assessment. South East and East facing aspects, slope >60 degrees, elevation ranges 1300 - 1700 m, phylitic rocks and agricultural land followed by forest are the major contributors of landslide hazard in the study area. The weight of evidence model was validated by using area under curve method. The success rate curve showed the accuracy of 73.16%. It can be concluded that weight of evidence model is suitable model for landslide susceptibility analysis and the area is highly susceptible to landslide occurrence.

MODELING SPATIAL DYNAMICS APPLIED TO THE ASSESSMENT OF URBAN GROWTH IN THE VICINITY OF PIPELINES IN RIO DE JANEIRO

The use of mathematical models in environmental studies allows significant scientific contributions to the physical planning of an area, since they help to understand the impact of changes in land cover and predict future trends of changes in ecosystems. The study area includes portions of the municipalities of Duque de Caxias, Nova Iguacu, and Belford Roxo, located in the state of Rio de Janeiro, Brazil, and aims to carry out experiments in land cover changes in the medium-term (10 years) within the catchment area of ORBEL pipelines. The modeling performed in this study was developed using the software Dinamica EGO. For model calibration, we used the weights of evidence method. Positive values foster certain transitions, while negative values indicate low probability of transition. Model validation was executed by means of the fuzzy similarity method using exponential decay. The results show that for the study period (1998-2010) the rate of change for the transition of the class 'others to woody vegetation' is much higher than for the other class transitions observed in the same periods. One annual land cover scenario was generated for the year 2020. The scenario indicated the growth of suburbs near the ring road around Rio de Janeiro, especially in the municipality of Duque de Caxias.

GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods

ABSTRACTLandslides cause considerable loss of life and property; therefore, it is important to determine landslide susceptibility. The aim of this study is to produce a geographical information system (GIS)-based landslide susceptibility map (LSM) by the frequency ratio (FR) and weight-of-evidence (WOFE) methods for the selected study area (Şavşat city, Turkey), to calculate the accuracy of the produced LSM, and to perform all these operations fully automatically. User-interface programs were developed in a GIS environment. Slope, aspect, elevation, lithology, distance to fault, distance to river network, normalized difference vegetation index, land use/land cover, and rainfall parameters were included to produce the LSM. The area-under-curve value obtained in receiver operating characteristic (ROC) analysis of the LSM produced by the FR method is 0.78 and that obtained in ROC analysis of the LSM produced by the WOFE method is 0.74. The LSM production process typically takes a long time. This study presents a different perspective on the production of LSM by developing a new method to automatically perform landslide susceptibility analysis. The method presented here, which includes user-interface programs, could be applied to any landslide area.

Analysis Geological and Geophysical Data for Prediction Landslide Hazard Zone with Weight of Evidence Method in Pacitan District East Java

Pacitan district have an interesting anomaly. Every time mostly impacted by disaster especially landslide. Landslides in their various forms are common hazard in mountainous terrain, especially in seismically active areas and regions of high rainfall. Landslides are one of the most common natural hazards in the Southern Range East Java terrain, causing widespread damage to property and infrastructure, besides the loss of human lives almost every year. The aim of this study predicted the potential landslide using Weight of Evidence Method. The geological data used lithological data, structural data, contour data and, alteration. Results from this data analysis are six evidence maps, such as NE-SW lineament, NW-SE lineament, host rock, heat source, kaolinite alteration and iron oxide alteration maps. The geophysical data analysis the distribution of rock density to interpretation the landslides. Evidence maps were analyzed by weight of evidence methods to result in favorable maps where the validity was tested using conditional independence (CI), the pairwise and overall tests. Then, the analyses produced a posterior probability map of the landslide. Posterior probability map (mineral potential maps) was validated by checking field. Posterior probability map (after validation) or favorable map predicted approximately favorable zone and non-favourable zones. Favorable zones of Potential Landslide Hazard Zonation, are divided into three classes. They are high-potensial hazard, moderate hazard and low hazard.
 
 Keywords: Pacitan, GIS, Weight of Evidence, Landslide

Distinctive controls on the distribution of river-damming and non-damming landslides induced by the 2008 Wenchuan earthquake

The 2008 Wenchuan earthquake (China, Mw 7.9) highlighted the importance of assessing and mitigating the hazards from co-seismic landslides and landslide dams. The seismic shaking triggered hundreds of thousands of landslides, about 800 of which dammed the course of rivers. To understand whether distinctive factors concurred with the river-damming events, we analyzed the spatial patterns of the river-damming landslides and the non-damming landslides separately, with reference to a number of possible controlling factors. Then, we quantified the significance of these factors using the weight of evidence method, and we used the results to perform a susceptibility assessment in a portion of the earthquake-affected region to verify the effectiveness of the method. We find that the distance to the fault surface rupture, peak ground acceleration (PGA) and lithology play a controlling role for co-seismic landslides of any type. The occurrence of river-damming landslides, rather than by a specific lithology or topography, is more related to hydrological factors, while topographic controls (slope, internal relief and terrain roughness) are more significant for the non-damming landslides.

GIS-Based Approaches for the Landslide Susceptibility Prediction in Setif Region (NE Algeria)

The present study is focused on a comparative evaluation of logistic regression (LR), frequency ratio (FR), information value (IV), and weight of evidence (WoE) methods for landslides susceptibility assessment in Bouandas region, North of Setif (NE Algeria). Information about landslide inventory and 17 pre-defined causative factors were prepared from multiple sources. The four methods are used to derive the weighted value of causative factors along the study area. The results were validated using receiver operating characteristic and the areas under the curves obtained using the FR, LR, IV and WoE methods are 0.86, 0.84, 0.81 and 0.79, respectively. The landslide susceptibility map produced from FR model is proposed to be more useful for the study area. It could reveal the relative importance of different factors in explaining landslides, and it may assist engineers in land-use planning.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

Spatial Potential Analysis of Earthquakes in the Western Himalayas Using b-value and Thrust Association

ABSTRACT In this study, we have analysed the spatial variation of b-values (from frequency-magnitude distribution (FMD)) in the western Himalayas as an indicator to demarcate the potential zones of earthquake occurrences. This is done under the acceptance of interpretation that decrease of b-values is correlated with a stress increase in the epicentral region of an approaching earthquake event. In addition to this, the spatial association of the earthquake epicenters with the major thrusts in the region using weights of evidence method, to identify potential zones of earthquake occurrences have also been analysed. Both analyses were carried out using a historical earthquake (Mw> 4) database of the1900-2015 period. Finally, based on the spatial variation of b-values and ‘contrasts’ derived from weights of evidence method (thrust associations), the derived map information was geospatially combined to prepare a “spatial earthquake potential” map of the western Himalayas. This map demarcates the western Himalayas into 3 zones - high, medium and low potential for future earthquake occurrences.