جستجوی مقالات مرتبط با کلیدواژه « weight of evidence » در نشریات گروه « جغرافیا »

Flood is considered as one of the most destructive natural disasters worldwide, because of claiming a large number of lives and incurring extensive damage to the property, disrupting social fabric, paralyzing transportation systems, and threatening natural ecosystems. Flood is one of the most devastating natural disasters causing massive damages to natural and man-made features Flood is a major threet to human life (injure or death of man and animal life), properties (agricultural area, yield production, building and homes) and infrastructures (bridges, roads, railways, urban infrastructures). The damage thet can occur due to such disaster leads to huge economic loss and bring pathogens into urban environments thet causes microbial development and diseases Therefore, the assessment and regionalization of flood disaster risks are becoming increasingly important and urgent. Although it is a very difficult task to prevent floods, we can predict and compensate for the disaster. To predict the probability of a flood, an essential step is to map flood susceptibility.
The methodology of the current research is includes the following steps:Flood inventory mapping;
Determination of flood-conditioning factors;
Modeling flood susceptibility and its validations.
 Et first , 146 flood locations were identified in the study area. Of these, 102 (70%) points were randomly selected as training data and the remaining 44 points (30%) cases were used for the validation purposes. In the next step 1 flood-conditioning factors were prepared including geology, landuse , distance from river , soil , slope angle, plan curvature, topographic wetness index, Drainage density elevation, rainfall. Then, the probability of the flood occurring for each class of parameters was calculated. Et the end, the obtained weights for each class in the Geographical Information System (GIS) were applied to the corresponding layer and flood risk map of th studied region was prepared. Subsequently, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps.
To determine the level of correlation between flood locations and conditioning factors, the FR method was used. The results of spatial relationship between the flood location and the conditioning factors using FR model is shown in Table 2. In general, the FR value of 1 indicates an average correlation between flood locations and effective factors. If the FR value would be larger than 1, there is a high correlation, and a lower correlation equals to the FR value lower than 1.
The analysis of FR for the relationship between flood location and lithology units indicates thet Cenozoic group has the highest FR value. In the case of land-use, it can be seen thet the residential areas and agriculture land-use have values. One of the most important factors affecting the flood is distance from the river. The results showed thet the class of >500 m FR was the most effective one. The analysis of FR for the relationship between flood location and slope angle indicate thet class 0-6. 1 has the highest FR value. In the case of slope aspect, flood event is most abundant on flet and East facing slopes According to the analysis of FR for the relationship between flood location and plan curvature, flet shape has the highest FR value., A flet shape retains surface run-off for a longer period especially during heavy rainfall . Flood locations are concentrated in areas with a TWI >6. 8 drainage density > 4. 6 km/km2 and altitude classes of 1200 m. In the soil layer, the tallest weight is from the earth with a small transformation of gravel. Finally, the maximum weight is the maximum rainfall.
In this study, all parameters of WofE model were calculated for each conditioning factor. In the lithology unit, the Cenozoic class has the highest flood susceptibility. Among the different land-use types, agriculture categories had the highest values . The distance from the river from 0 to 1000 m indicated positive influence in flooding, while the areas more than 1000 m or far from the river represented the negative correlation with flood occurrence. In the soil layer, clayey soil and tuberous soil had the highest weight. The analysis of WofE for the relationship between flood occurrence and slope angle indicated thet slope angle from 0 to 6. 21 had positive influences in flooding. In the case of slope aspect and plan curvature, flet area had a strong positive correlation with flood occurrence. Effectiveness increases wit increasing TWI classes. The results of drainage density indicate thet areas with higher drainage densities are more susceptible to flood occurrence. By increasing the height of the flooding reduced sensitivity classes. byn flooding rainfall and flood events increased with increasing rainfall.
 
The prediction accuracy and quality of the development model were examined using the area under the curve (AUC). Specifically, the receiver operating characteristic (ROC) curve was used to examine the basis of the assessment is true and false positive rates . So the results showed thet based on the area under the curve, the FR and WofE models show similar results and can be used as a simple tool for verifying the map prepared for flood sensitivity and reducing its future risks.
Floods are the most damaging catastrophic phenomena in the worldwide. Therefore, flood susceptibility mapping is necessary for integrated watershed management in order to have sustainable development. In this study, flood susceptibility zones have been identified using FR and WofE methods. Et first step, a flood inventory map containing 146 flood locations was prepared in the kermanshah Province using documentary sources of Iranian Water Resources Department and field surveys. Then, eleven data layers (lithology, landuse, distance from rivers, soil texture, slope angle, slope aspect, plan curvature, topographic wetness index, drainage density, and altitude) were derived from the spatial database. Using the mentioned conditioning factors, flood susceptibility maps were produced from map index calculated using FR and WofE models, and the results were plotted in ArcGIS. Finally, the AUC-ROC curves using validation dataset were prepared for the two models to test their accuracy. For this reason, of 146 identified flood locations, 102 (70%) cases were used as training data and the remaining 44(30%) was used for validation. The validation of results indicated thet the FR and WofE models had almost similar and reasonable results in the study area. Based on the overall assessments, the proposed approaches in this study were concluded as objective and applicable. The scientific information derived from the present study can assist governments, planners, and engineers to perform proper actions in order to prevent and mitigate the flood occurrence in the future.

Introduction :

The Zagros fold-thrust belt belongs to the part of the Alpine-Himalayan system, represented by the southern Zagros-Dinaride branch of the orogenic belt. This is an orogenic segment NW-SE trending to a distance of nearly 2000 km and structurally consists of many synclines and anticlines. However, as basement faults are hidden from view under the more recent sedimentary units scarcely reach the surface. As a consequence, the identification and study of basement faults has mostly relied on indirect information such as anomalies in topography. Within the Zagros fold-thrust belt, there are many pierced salt plugs that are known as Hormoz series. Hormoz Salt Basin includes many diapirs of Cambrian salt that have risen through the Permian to Recent sediments. The aim of this paper is identification of basement faults in the Zagros fold-thrust belt using interpretation of satellite images, and determination of relationship between basement faults and salt plugs of Hormoz series using Geographical Information System (GIS) techniques such as the weights of evidence modeling. 

Methodology:

 In this research, we examined many techniques of remote sensing for the recognition of faults, and then we did field checking to determination of the accuracy of the work. Remotely sensed data, including Landsat Enhanced Thematic Mapper plus (ETM+) images, were used to obtain information on structural features and for deriving lineaments of the study area by production of color composite images and applying some filters, such as Laplacian and Sobel and also some directional filler. Usually, the basement faults are hidden by sedimentary cover, and their location is uncertain, but there is some evidence for identification of them, such as deflections in trends of fold axes, offset markers and alignments of salt diapirs. In the study area, most of the folds have a general east-west trend that some detachment folds are cut by basement faults. There are deflection in general trend of folds and have been classically interpreted as the effect on the cover of strike-slip movement along underlying basement trends. Some conceptual models for the evolution of the fold structures affected by basement faults, offered by Leturmy et al. that in this research are confirmed.

 Results and discussion:

 In the studied area, 34 normal and strike slip faults were recognized. Some of these basement faults could have an important role for salt uplifting. Fault No.1 is strike-slip fault with 26o azimuth crosses Larak salt plug and Hormoz salt plug and caused deviation and sinistral displacement of east of Namak anticlinal axes. Based on this fault activity, some minor faults formed parallel to the major fault. Fault No. 2 the strike-slip fault with 129o azimuth caused a deviation of the Faraghon and Namak anticline axes. This fault crosses Darbast, Takhu, Kushk kuh-West, and Gahkum salt plugs. In some tectonics text, this fault is known as Oman line. Fault No.3 This fault is known as a Minab fault, the trend of the Minab anticline and the deviation of that axis can be considered as this fault's function. The fault has a 165o azimuth. However some of the mentioned basement faults, such as fault numbers 1, 9, 11, 13, 15, 16, 17, 18, 19, 23, 24, 26, 28 and 33 may already identified, but the influence of them on the salt diapirism in the Zagros region have not been discussed. Based on the result of weights of modeling method, there is positive spatial association between the Basement faults and the salt diapirs as indicated by the contrasts C. So, findings of this research to be consistent with the aim of the plan. The positive spatial relationship is statistically considerable within 1000 to 10000 m; so, following to the highest Studentised C, it is optimal within 1000 m.

 Conclusion:

 The interpretation of satellite images based on remote sensing techniques such as shaded-relief images analysis, filtering, and deflections in trends of fold axes, shows that some structures have a character of regional photolineaments (especially NNW-SSE and NE-SW trending. Such structures were considered to be main fault systems of the study area. Finally, 34 basement faults have been identified that among them 14 faults are introduced as the first time. Also, there is a rectilinear pattern of salt diapir emplacements. These implicit lines of weakness are approximately indeed related to basement structural trends, based on weights of evidence method; pierced salt diapirs are associated spatially with basement faults within a distance of 1 km. Also among 123 detected salt diapirs in the region, forty-five of them, 36 percent, have a maximum relationship with basement faults. So, tectonic condition is an important factor in the exposure of salt diapirs in the study area.

Landslides are amongst the most damaging geologic hazards in the world. They pose a threat to the safety of humankind lives as Well as the environment, resources and property. Compared with other natural hazards such as volcanic eruptions and floods, landslides cause considerable damage to human beings and massive economic losses (Guzzetti, 2005). According to preliminary estimates, about 500 billion riyals annual are caused economic damage in Iran by landslide occurrence (Hosseinzadeh et al., 1388:27). Much literature available on landslide hazard assessment methodologies broadly falls into three main Approach groups: qualitative, quantitative and artificial intelligence (AI) approaches. in general, a qualitative approach is based on the subjective judgment of an Expert or a group of experts whereas the quantitative approach is based on mathematically rigorous objective Methodologies. Artificial Intelligence (AI) techniques can make use of heuristic knowledge or pattern matching technique as opposed to solving a set of mathematical equations. The AI broadly covers Artificial Neural Networks (ANN), Expert system, and other heuristic knowledge-based or rules-based techniques. (Neaupane and Piantanakulchai 2006:281). For the Landslide Susceptibility Mapping can be used a variety of models, such as logistic regression, Analytical Hierarchy Process (AHP), Analytic Network process (ANP), artificial neural network, the bivariate statistical models, LNRF, fuzzy logic models and etc. Usually choose the most appropriate approach and model is done based on the data type, the scale of the study area, the scale of analysis and Knowledge of researcher. Study area: In this study siyahrood catchment has Zonation for landslide susceptibility by using weights of evidence models (Baye's theorem). The basin is located in the province of Gilan. The catchment area is 437 km and is sub-basins of the Sefidrood River. The weight of evidence (WofE) method (Bonham-Carteretal.,1989) has been used to evaluate shallow-landslide susceptibility and has been tested as a useful spatial data model for various applications including mass-movement studies, mineral research, and groundwater spring mapping (Mark and Ellen, 995; Poli and Sterlacchini,2007; Barbieri and Cambuli, 2009).. It takes into account the relationships existing amongst the occurrence of a supporting evidence (shallow landslides in this study) and the distribution of causal factors (shallow-landslide predisposing factors in this study) The WofE is a statistical method based on the Baye's theorem (Denison et al., 2002). This methods first applied to mineral exploration in1988 (Bonham-Carter et al., 1988) and Subsequently, Van Western (2002) applied the method for Landslide susceptibility assessment. Bayes’ theorem can be written as: Equation (1) P(s│B_i)= (P(B_i│s)×P(s))/(P(B_i)) Where P (Bi | s) is the conditional probability to have Bi given s, P (s) is the prior probability to find s within the study area (AS) and P (Bi) is the prior probability to find the class Bi within the study area AS. In this study landslide susceptibility zonation has been done using several natural and anthropogenic parameters Such as (lithology, distance from fault, distance from river, rainfall, land slope, aspect, land use, vegetation density (NDVI) and sediment transport index (STI) stream power index (SPI) and topographic wetness index (TWI)). After the Weights classes were obtained using the model for each parameter, Weights was applied to each class in the Arc map software and eventually with overlay parameters was obtained landslide susceptibility maps. The final Maps was classified In 5 susceptibility class using the method of natural breaks (very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility and high susceptibility). According to the results of the model and the map developed in the lithology layer, Most of the weight is allocated to Class B (old alluvial terraces and High alluvial fans). Moderate range among the different classes of land use and north and northwest directions in the aspect parameter Have the greatest impact on landslide occurrence. As well as slope of 20-10 degrees and 10-5 degrees, respectively and in the layer distance from the river, 100 meters from the river have the greatest impact in landslide occurrence. Assessment models with using landslides occurred in the area show that with increasing risk class, landslide density in the class increases And 59 % of landslide, has occurred in very high susceptibility class. While the area of this class compared to total area of the region is only 10.5 percent. Although Classes with very low susceptibility, low and moderate susceptibility are included approximately 71 percent Area of a Region, But only a small portion of the landslides occurred (16.7%) in these classes While the roughly 83. 3 percent of landslides occurred in the area are located in the fourth and fifth class (high and very high susceptibility). Due to this can be said that the model has a good functionality in the area terms of the prediction of landslides.