Weights Of Evidence Method Research Articles

Landslide Susceptibility Mapping Using Weight of Evidence Method in the Purwanegara District and Surrounding Areas, Banjarnegara Regency, Central Java Province, Indonesia

Abstract The research location is in the Purwanegara Subdistrict, Banjarnegara Regency, Province of Central Java. According to the Geological Agency of Indonesia, this location is a landslide-prone area, and many landslides occur yearly. Therefore, it needs a landslide susceptibility map is basic information for landslide mitigation in this area. The parameters used in this research are slope, lithology, distance from structural geology, distance from the river, land use, and rainfall. Using six parameters, the weight of evidence (WoE) method was used to develop a landslide susceptibility map. This research aims to make a zonation of landslide susceptibility in this area. The data were collected by field survey and visiting some related institutions. The landslide data used are 85 landslide events in the last three years. All data was then randomly divided into two groups: 70% as model data sets and 30% as validation data sets. The map of every parameter will be overlaid to create a landslide susceptibility zone. The Area Under Curve (AUC) method validated the final map. The result shows that landslide susceptibility zonation can be divided into four zones: very low susceptibility zone, low susceptibility zone, medium susceptibility zone, and high susceptibility zone. The very low susceptibility zone is mostly located in the north, the low susceptibility zone is mostly in the north and south, the moderate susceptibility zone is in the middle, and the high susceptibility zone is in the middle of the research’s location. The validation results of the success and prediction rates using the AUC method show an accuracy value of 0.86 and 0.82, respectively. According to the validation result, both can be classified as very good.

Vulnerability of Mass Movement Zonation Using DEM in Upstream – Midstream Kelara Watershed, Jeneponto Regency, South Sulawesi

Mass movement is one of the major natural disasters that claim many lives and property. The Kelara watershed, which is the research area, often encounters land movements that often cause casualties. The problem of mass movement often occurs in the upstream - midstream part of the Kelara watershed with mountainous to hilly morphology with moderately steep to steeply sloping relief. The parameters used in this analysis are lithology, distance from faults, distance from rivers, distance from streets, land use, rainfall, soil type, slope, profile curvature, and slope aspect. The analysis was conducted by giving weights based on the comparison of soil movement events to the class of each parameter using the Weight of Evidence method. Based on the Weight of Evidence method, the mass movement vulnerability zone is divided into five zones, very low vulnerability zone occupies 7.75%, low vulnerability zone occupies 27.11%, medium vulnerability zone occupies 31.76%, high vulnerability zone occupies 21.72%, and very high vulnerability zone occupies 11.66% of the total study area. Based on validation using the AUC value, the Weight of Evidence Method has a value of 0.771 (Fairly Good Model).

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.

The CHANGE (Climate Health ANalysis Grading Evaluation) tool for weight of evidence reviews on climate change and health research

BackgroundClimate change has been identified as one of the biggest threats to human health. Despite this claim, there are no standardized tools that assess the rigor of published literature for use in weight of evidence (WOE) reviews. Standardized assessment tools are essential for creating clear and comparable WOE reviews. As such, we developed a standardized tool for evaluating the quality of climate change and health studies focused on evaluating studies that quantify exposure-response relationships and studies that implement and/or evaluate adaptation interventions.MethodsThe authors explored systematic-review methodology to enhance transparency and increase efficiency in summarizing and synthesizing findings from studies on climate change and health research. The authors adapted and extended existing WOE methods to develop the CHANGE (Climate Health ANalysis Grading Evaluation) tool. The resulting assessment tool has been refined through application and subsequent team input.ResultsThe CHANGE tool is a two-step standardized tool for systematic review of climate change and health studies of exposure-response relationships and adaptation intervention studies. Step one of the CHANGE tool aims to classify studies included in weight-of-evidence reviews and step two assesses the quality and presence of bias in the climate change and health studies.ConclusionThe application of the CHANGE tool in WOE reviews of climate change and health will lead to increased comparability, objectivity, and transparency within this research area.

Landslide Analysis of Western Lombok Island Using Weight of Evidence (WoE) Method

Landslides are widely distributed in all parts of Indonesia. West Nusa Tenggara Province is one of Indonesia's provinces with a quite high landslide distribution. This research was conducted in the western part of Lombok Island, including West Lombok Regency, Mataram City and North Lombok Regency, which has a moderate to high landslide risk class based on the 2021 Indonesian Disaster Risk Index (IRBI) data, so it is necessary to mitigate landslides by making a landslide vulnerability map. This research aims to determine the main factors of landslides in western Lombok Island, create a landslide vulnerability level map and analyse the distribution of landslide-prone areas in western Lombok Island using the weight of evidence (WoE) method. The research methods used are the literature study method (collecting information about land movement events in the research location and other information about the theories needed and used during the research) and the ArcGIS-based WoE method. This research shows that the slope parameter is the main factor of ground movement based on the analysis result of the WoE method. The landslide vulnerability level of the WoE method and the distribution of landslide occurrence points are high vulnerability level with an area of 473.45 km2, medium with an area of 198.88 km2, low with an area of 210.58 km2 and very low with an area of 911.73 km2.

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.

Landslide Susceptibility Assessment Using the Integration of Frequency Ratio and Weight of Evidence Model in North Luwu, South Sulawesi, Indonesia

The Masamba area of North Luwu Regency has been hit by a flash flood disaster on July 13, 2020. The flash flood claimed dozens of lives, hundreds of people were injured and thousands of houses were submerged. Based on the analysis of several information, the cause of the flash flood in Masamba was due to high rainfall and landslides that occurred in the upstream area. Land movement vulnerability zoning is needed in this area to provide information in making disaster mitigation policies for post-disaster planning and development. Frequency Ratio and Weight of Evidence methods were applied in this study by using parameters such as: slope, lithology, land cover, rainfall, slope direction, slope convexity, distance from river, distance from fault and soil type. The Weight of Evidence method provides more accurate results with an accuracy rate of 99.83%. Vulnerability zoning of landslides in the Masamba area are grouped into 5 classes; The zone of vulnerability to soil movement is very high, around 6,822.27 ha or about 25.47% of the research area. The high ground movement vulnerability zone is about 4261.84 ha or about 15.91% of the research area.

Development of a flexible self-calculation scoring model to determine stroke occurrence

Stroke has become a significant threat to global public health, the ideal solution to which is primary prevention. Identification and management of determinants of stroke among various variables in different datasets are essential steps for its primary prevention. This study aimed to develop a flexible scoring model, which can easily modify different datasets. The public dataset containing 41,931 cases with 643 occurrences of stroke was randomly divided into training, validation, and test datasets comprising 25,158 (60%), 8,386 (20%), and 8,387 (20%) cases, respectively. Three continuous variables (age, body mass index, and average glucose level) and seven categorical variables (heart disease, hypertension, sex, married/smoking/work/residence status) in the dataset were converted using the weight of evidence method. The significant variables among 10 transformed variables were selected using multivariable logistic regression analyses. The scoring model for stroke occurrence was developed in the training and validation datasets, and performance was evaluated in the test dataset. Age, average glucose level, heart disease, and hypertension were significant variables of stroke occurrence. The scoring model was easily calculated using four determinants and indicates that the stroke occurrence ranged from 0.04 to 12.50%. The performance of the scoring model on the test dataset was similar to that on the validation dataset. This novel point scoring model is flexible enough to modify various datasets and can be used for determinant identification. Furthermore, its simplicity allows individuals to manage determinants by self-calculating stroke occurrence. Our model contributes to primary prevention using determinant identification and management.

Thermal Hazards Evaluation Based on Weight of Evidence Method in the Resource Area of Datong River in Qinghai-Tibetan Plateau

With global warming and increasingly frequent human activities in permafrost regions, it is of great significance to accurately and scientifically evaluate the probability and scope of thermal hazards in permafrost regions. Based on remote sensing image interpretation and field survey, the weight of evidence method (WoEM) was used to comprehensively evaluate the risk of thermal hazards in the source area of the Datong River. There were 10 factors, such as ground ice, mean annual ground temperature, mean annual air temperature, and ground soil type etc., selected in the WoEM. The results showed that the thermal hazard occurrences were closely influenced by ground ice, mean annual ground temperature, ground soil type, etc. The thermal hazards mainly occurred in the unstable permafrost with MAGT of –0.5 to –1.5 °C, accounting for 54.72% of the thermal hazards. The distribution area of thermal hazards in ground ice Level I and II accounts for 66.42%. Thermal hazards mainly occur in the soil types of bog soil and sapropel bog soil, accounting for 41.24% and 29.62% of the total thermal hazards area, respectively. Based on the influence factors and WoEM of thermal hazards occurrence, the probability map of thermal hazards occurrence in the source area was obtained. Additionally, the characteristics of the region with a high probability of thermal hazards occurrence and their causes were also comprehensively analyzed.

Comparison of two landslide hazard zonation methods in the volcanic terrain of Temanggung Regency, Central Java, Indonesia

Landslides are a recurring phenomenon that disrupts the natural environment and causes yearly property damage, economic losses, and fatalities. The damage is expected to increase due to deforestation rates, population growth, agriculture, slope-building infrastructure expansion, and global climate change. This study assesses the susceptibility to landslides through Weight of Evidence (WoE) and Frequency Ratio (FR) methods in the Temanggung Regency, Central Java Province, Indonesia, that located on the slopes of two active volcanoes. Initially, a landslide record and the input parameters of the landslide controlling factors were prepared from field surveys, remote sensing data, and secondary data and processed by a geographic information system (GIS). Six landslide parameters in thematic layer maps were selected to develop landslide susceptibility: slope, lithology type, geological structure density, land cover, and rainfall. According to the WoE and FR models, a landslide susceptibility zoning map was classified into four landslide-prone zones from low to very high. Finally, the success and predictive rate curves method confirmed the landslide susceptibility maps to check the model accuracy. The results showed that the landslide susceptibility map using the WoE method had better accuracy than the FR method, with a success rate of 78.48% and a prediction rate of 81.1%. In comparison, the FR method was 74.53% for the success rate and 78.48% for the prediction rate. These landslide susceptibility maps can be used as a guideline to develop land-use planning and landslide disaster mitigation.

A COMPARATIVE STUDY OF THE BIVARIATE STATISTICAL METHODS AND THE ANALYTICAL HIERARCHICAL PROCESS FOR THE ASSESSMENT OF MASS MOVEMENT SUSCEPTIBILITY. A CASE STUDY: THE LM-116 ROAD – PERU

It has been long observed that the Peruvian Central Highway (PE-22) and the LM-116 road are among the roads most affected by mass movements (MM) in Peru, frequently exposed to the occurrence of rockfalls, debris flow and landslides; both roads represent an important connection alternative between Lima with towns, cities and mining centers located in the Central Mountain Range of the Andes. In this research, firstly, a point density analysis was performed using Geographic Information Systems (GIS) considering the road network of all of Peru (composed of 144,499 km) and the inventory of geological hazards (GEOCATMIN) prepared by the Geological, Mining and Metallurgical Institute of Peru INGEMMET (2000-2018). Subsequently, the evaluation of the mass movement susceptibility on the LM-116 road has been carried out using free access data reported by Peruvian institutions (INGEMMET, MTC, MINAM) from which it was possible to elaborate thematic maps, including the most relevant factors in the occurrence of mass movements, like a slope, lithology, geomorphology, land use, drainage density, and the distance from tectonic structures. Finally, for the mass movement susceptibility analysis, three methods have been considered: the Analytic Hierarchical Process (AHP), the Statistical Index (Wi) and the Weights of Evidence (WoE). The results were validated using the area under the curve criteria (AUC). Both bivariate statistical methods (Wi and WoE) presented a prediction rate above 78%, with a higher rate for the WoE method. On the other hand, the semi-quantitative method (AHP) obtained values in the order of 69%. Therefore, it is concluded that the maps elaborated with the statistical methods presented a better approximation concerning the database of geological hazards reported by GEOCATMIN.

Flash Flood Susceptibility Mapping in Sungai Pinang catchment using Weight of Evidence

Flash floods are one type of natural disaster that frequently occurs in Malaysia, especially in urban areas. This paper’s objective is to develop the flash flood susceptibility mapping (FFSM) using weight of evidence (WOE) in the Sungai Pinang catchment. The Sungai Pinang catchment is located in the highly urbanised areas of Georgetown. The FFSM was developed using six flash flood conditioning factors: elevation, slope, rainfall, land cover, distance from the river, and lithology. The study showed that the WOE method is capable of producing high accuracy, with a success rate of 0.839 and a validation rate of 0.923 for the receiver operating characteristic’s area under the curve (AUROC).

The susceptibility of landslide zone using weight of evidence method in nagari tanjung sani and nagari sungai batang, west sumatera.

Landslides naturally occur to create slope stability, especially in mountainous areas. Disaster management is needed to minimize the impact of losses incurred. Management of landslide is formed based on knowledge of characteristics, types, and processes of disaster occurrence. Map of landslide susceptibility was one of that. This research aims to determine and analyze the type, landslide density based on event control factors, and zonation of susceptibility. The method used was the statistical method of Weight of Evidence (WOE) and field observation. The results showed that Nagari Sungai Batang has a translational and rotational type of landslide with a medium density of landslide and medium susceptibility of landslide. Nagari Tanjung Sani has landslide type rockfall and topple with a high density of landslide and highest susceptibility. Based on these conditions there were some differences in type, the controlling factor, and susceptibility of landslide degree, although they were formed from the same geomorphological process as Caldera shape by Andesit Maninjau Formation.

Inference of Submerged Macrophytes Colonization Using the Weights of Evidence Method

In some tropical reservoirs, aquatic macrophytes have covered extensive areas and it is difficult to monitor, control and forecast their proliferation. The major issues are economic and ecological because macrophyte growth has caused significant financial losses to hydroelectric power plants and has affected the ecological balance. Thus, this work aimed to infer the most likely areas to be colonized by Submerged Aquatic Vegetation (SAV), using influencing factors, such as the morphometric aspects of the area and any preexisting vegetation, to support the management decision-making process. Four field surveys were carried out to collect hydroacoustic data, which indicate presence of SAV, in Taquaruçu reservoir, Paranapanema River, Brazil. The inference procedure was applied using the weights of evidence method. The results showed that depths up to 6 m have a high probability of colonization and that points with five or six colonized neighbor’s cells, considering a regular grid, are also more likely to be colonized. The weights for the slope map behaved differently at each survey interval. The probability maps of colonization were generated and can be very useful in supporting the decision-making process of SAV management activities, such as concentrating the monitoring efforts for the high-probability colonization areas, in this case those which the lowest depths or next to colonized regions.

Siamese network based prospecting prediction method: A case study from the Au deposit in the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China

Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas.

Assessing Landslide Susceptibility in the Northern Stretch of Arun Tectonic Window, Nepal

The northern stretch of the Arun watershed in East Nepal is dominated by steep slopes and rugged topography and experiences extensive landslides each year. Reliable landslide susceptibility assessment can potentially be an important tool for risk evaluation and mitigation in the Himalayas. The present study explores two GIS-based bivariate statistical methods, the weight of evidence method and the frequency ratio method to assess the landslide susceptibility of the study area. Seven major variables including slope angle, slope aspect, slope shape, geology, stream proximity, stream power index and land use were examined as the major contributing factors to landslide occurrences in the assessment. The landslide susceptibility map produced from these two methods are characterized by four zones of very low, low, moderate and high susceptibility. The landslide percentage of each zone turns out to be consistent with the order of its susceptibility. The results suggest that the weight of evidence method is more sensitive to the spatial variation of relevant factors; both methods produced fairly reliable results, as confirmed by a success rate of 75% for the weight of evidence method and 71% for the frequency ratio method. The present study demonstrates that the quantitative assessment methods explored may have a promising potential for landslide assessment and prediction in the Himalayas.

Combination of seismic attributes using graph-based methods to identify the salt dome boundary

Geological structural and target stratigraphic units can be well distinguished and clearly presented in seismic data based on their physical characteristics. However, due to the inherent band limited and noise contaminated and sometime distorted image on seismic data, it is not straight forward to identify, delineate and image those targets in seismic data. In this regard, the seismic attributes are used as a tool for extracting valuable information and characteristics from the seismic data for further appropriate geological interpretation of the target. In large size 3D seismic data, extreme interaction time with costly labor force is required for the manual interpretation of the seismic images. Automation of this procedure besides intelligent analysis of large number of various combination of attributes with different computational algorithms provides a reliable, fast and high productive alternative path. In this study, an automatic object delineation method based on combination of seismic attributes is developed based on the graph theory, mainly to identify the salt dome boundary in seismic data. Results are compared with the MLP and weight of evidence methods. For this purpose, initially a set of attributes with different window lengths are extracted from the data and then by using the principal component analysis method, the most appropriate set of attributes for combining are determined. Then a corresponding graph is derived for each attribute and the distance matrix of each graph is calculated and finally the similarity matrix is defined. Subsequently, the graphs are combined using the distance method. The binary model then is obtained by applying a threshold and image segmentation technique. To evaluate the results, they are compared with the interpreted models from the leading-edge methods, and for quantitative comparison, the accuracy value of the method then is calculated pixel by pixel. Based on the quantitative comparison of results, the accuracy obtained by the proposed method here is acceptable and is higher than the well-known MLP and weight of evidence methods, which shows the efficient application of the graph-based combination method in combining seismic attributes and delineation the salt dome boundary.

Landslide Susceptibility Mapping: An Integrated Approach using Geographic Information Value, Remote Sensing, and Weight of Evidence Method

Landslide Susceptibility Mapping: An Integrated Approach using Geographic Information Value, Remote Sensing, and Weight of Evidence Method

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.

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.