جستجوی مقالات مرتبط با کلیدواژه "landslide hazard zonation" در نشریات گروه "جغرافیا"

Landslides are influential factors in human life that are not well-known. Several factors have contributed to the occurrence of landslide that could increase the risk of landslide in any area. Identifying these factors and their value can help to appropriate landslide zonation. The classification of areas susceptible to sliding and hazard zoning is an important step in assessing environmental hazards and plays an indelible role in the management of catchment areas (Sakar, 1995). Therefore, knowing the most important factors affecting slip instability and slipping will help us to make developmental plans using appropriate methods. Therefore, by using statistical models, their vulnerability to landslide is identified and zoned by assessing and validating them. Landslide inventory map is the best method for designing a landslide hazard map based on aerial photo interpretation, field surveys, and historic landslides. Then, the spatial distribution of mass movements is presented as a point or polygon on the map. The purpose of this research is to investigate various and effective factors in the occurrence of landslides, as well as to evaluate and compare the effectiveness of statistical models in landslide hazard zonation in Nazlochai basin and introducing the most appropriate methods.

In order to investigate the landslide susceptibility zonation, the provision of a landslide inventory map is the most important part of the work, which can be done by using of geographic information systems with high accuracy. The accuracy of landslide zonation is largely dependent on this stage. So, at first, the existing landslides were identified by using various tools including aerial photos, satellite imagery (Google Earth), existing information, GPS, and in particular field surveys. In the present study, ten factors affecting the occurrence of landslides were considered: elevation, slope, gradient direction, distance from the waterway, distance from the road, distance from the faults,   lithology, land use, rainfall and vegetation index .For landslide zonation, bivariate statistical models, including Gupta-Joshi model with its correction method, information value method, and surface density method have been used.

For landslide hazard zonation using the Bivariate Statistical Models, various factors including elevation, slope, gradient direction, distance from the waterway, distance from the road, distance from fault, lithology, landuse, rainfall and vegetation index were studied. Existence and density of landslides in the western slopes show the role of geological formations, the distance from the waterway and precipitation in the occurrence of landslide. To evaluate the accuracy of the Bivariate Statistical Models, the density ratio index and the quality sum index were used. The more distinction between risk classes is, the model is more capable, and the quality sum index is used to compare the performance of different models. Finally, with respect to the resulting values, the zoning with the information value and surface density models were found to be desirable for the studied area.

According to the results of zoning (using the Bivariate Statistical Models), lithology, distance from the waterways and precipitation are the most important factors controlling the landslide occurrence in the studied area. Particularly lithologic factors are of great importance. Most of the landslides in the study area occurred on limestone and conglomerate, which are similar to the results of the research Amir Ahmadi who worked for Iran, while these formations do not have enough area in the basin. Limestone and a small amount of dolomitic limestone with an occupancy level of 15.5% of the basin, contain more than 30% of landslides. More importantly, limestone is coinciding with north orientation that confirms the role of gradient direction in occurrence of landslides. Although some scholars ignore the role of gradient direction (A. Gemitzi, 2011), other researchers (Carrara et al., 1991; Roostaei et al., 2017) have taken it into account in their research. The impact of the human factor mainly depends on changing environmental conditions, such as road construction, inappropriate plowing, excessive grazing and water diversion for agricultural use. Therefore, by studying the researches in Iran and in different parts of the world, the slipping factors in different basins and regions are not the same and in fact, different slip conditions are present in different regions.

Several factors have contributed to the occurrence of the landslide that could increase the risk of landslide in any area. Identifying these factors and their value can help to appropriate landslide zonation. The aim of the study is to find ways to reduce the damages caused by them, which makes it necessary to zoning the susceptible areas that play an undeniable role in watershed management. Therefore, by using statistical models and assessing them, the sensitive areas to the occurrence of landslide are identified. In this research, the landslide hazard zonation was performed based on the data-driven method. Based on this method, the zoning was done based on the use of slope data, aspect, elevation, precipitation, vegetation, geology, land use, distance to fault, distance to river, and distance to road. To validate the model, the ROC curve has been used which is a new and efficient method for verification. The purpose of this research is to investigate various influencing factors that affect the landslide occurrence in the Nazlochai basin.

In the methodology section, the satellite imagery processing (to identify and extract landslides, vegetation extraction, and land use) and logistic regression model have been discussed for landslide hazard zonation. In this study, by reviewing the previous sources (Mir Nazari, et al., 1393, Abedini, et al., 1393, Ayalew, et al., 2004, Ebadinejad, et al., 2007) and by investigating various factors (morphometric, climatic, and human) in Nazlochai basin, ten effective factors (elevation, slope, aspect, distance to river, distance to road, distance to fault, lithology, landuse, precipitation, and vegetation) on the landslide occurrence in the area were considered. The ArcGIS software was used to digitize and provide information layers for landslide hazard zonation, and the ENVI software was used for image processing, vegetation extracting, and land use mapping. Existing landslides were identified and characterized using various tools including aerial photos, satellite imagery (Google Earth), existing information, Global Position System (GPS), and field surveys.

The obtained coefficients indicated that the occurrence of landslide in the studied area had a direct relation with lithology, slope, and aspect factors, and weak relation with landuse, distance to fault, precipitation and distance to river. Lithology investigation of the region indicated that the more landslides have occurred on calcareous and conglomerate stones, which could be due to the development of the slopes and the accumulation of destructive materials on them. Slope is one of the slippery factors due to gravity and decreasing shear strength of soil in slopes of more than 10% to 45% leads to instability which in most researches is considered as an effective factor, too. Also, north slopes are more susceptible to landslide than the southern slopes due to the reduction of normal pressure and shear strength of the soil. By considering the Pseudo R-square index (equal to 0.34), which is greater than the threshold (0.2), this model shows acceptable fit. The area under the ROC curve was equal to 0.958, which shows a strong correlation with predicted landslides by the logistic regression model. Finally, the study area was classified into 5 landslide hazard classes include very low, low, medium, high, and very high.

In this research, landslide hazard zonation has been done using the logistic regression model in the Nazlochai basin. The coefficients of variables indicated that the occurrence of landslide in the study area had a direct relationship with the lithology, slope, and aspect factors; and weak relationship with landuse and distance to fault. Thus this indicates the probability of landslide occurrence increases by changing in lithology, slope, and aspect

Extended Abstract Introduction Earthquakes and landslides are the most harmful natural disasters that have synchronization and time and space correlation with each other. This study was carried out in seismic zone affected by Arasbaran earthquake (Qaradagh), in which there were a lot of casualties and destruction. On 11 August 2012, the Varzeghan twin Earthquakes [Mw 6.4 and 6.2] struck Varzeghan, Ahar and Heris region in NW of Iran. It killed more than 306 people and a large number of people were injured and has caused increment in slope instability, and transformation of geomorphic processes into risk factors. Methodology In this research, landslide susceptibility was zonated by using two models of analytic network process (ANP) and logistic regression in Qoshadagh fault system and an appropriate model was introduced. Our analysis of geohazards distribution allowed evaluation of geomorphic, climate- hydrologic, human-land use and morphologic controls on earthquake-induced-land sliding, process mechanisms, and hazard process chains, particularly where they affected local populations. For this study, both the Sentinel-2A MSI and Landsat-8 OLI data (2017) were used. Fourteen effective factors of landslide (Lithology, Land use, Fault, DEM, Climate, Aspect, TWI, SPI, Slope, Road, River, Rain, LS & Soil) were generated in ArcGIS, and then weight of each factor was determined in Super Decisions software, and final zonation maps were regained from ArcGIS afterward. Results and discussion After preparing the standard maps and options, the network analysis method (ANP) and logistic regression were used to investigate the susceptibility of land slides. In order to implement the network analysis process, the related criteria layers were developed in ArcGIS software. Then, in the SuperDisign software environment, the main model of the network analysis process was designed based on completed questionnaires by the experts. Then, a logistic regression model was used to analyze the spatial relationship between the land slide event and the effective factors in this event. The maps related to the factors affecting the land slides of the study area, which are independent variables in the land slide event, were introduced into Idrisi-Selva software and processed for logistic regression modeling. Landslide distribution layers in the area were also converted to the binary map 0 and 1 by the Calculator Image function. This means that the slip pixels on the map are shown with the number 1 (slipping) of non-slip pixels with 0 (no slip). Finally, after entering the data into the logistic regression model, the coefficients of the model are extracted using the effective parameters in the Idrisi-Selva software. Distance from the fault and precipitation factors had the most, and Land Use Factor had the least effect on landslide occurrences. Occurrence of 62.2% and 71.1% of landslides in high and very high risk classes in ANP and logistic regression, respectively, indicated an acceptable accuracy for landslide prediction maps. Validation results of methods with ROC index showed that AUC of the maps in model was 85.52%, and in analytic network model it was 81.35%, with a standard error of 0.06; while both represented a very good predictive capability Conclusion The purpose of the present study was to study the ability of this model in comparison to other methods, in addition to modeling the sensitivity of Earthquake-Induced-Landslides using the network analysis method (ANP). Therefore, effective factors in the field of scaling were identified and in the Super Decision software environment, the weight of each factor was determined. Then, the weights received in ArcGIS software environment were converted to the final map of the zoning of the landslide. Accordingly, among the fourteen effective factors in the occurrence of landslides in the area, the distance from the fault has the most effective factor in the two models (ANP) and (LR) and has the highest coefficient of effect on land-slip occurrence while land use in Both models have the lowest coefficient. As expected, the main faults, in particular the fault and the fault lines, and interspersed with the twin Arasbaran earthquakes, as well as the northern and northeastern slopes, are more susceptible to instability. The map of the model implementation categorized the susceptibility of earth sculpting in 5 classes with very low, moderate, high and very sensitive sensitivity. Very high and high risk classes have been shown to be 6.6% and 13.4% of the area of the region, prone to hazardous landslides. These results show a high correlation and correlation with the model implemented in the logistic regression method, which is 5.8% and 17.7%, respectively. The occurrence of 62.2% and 71.1% of landslides in high and very high risk classes, respectively, in ANP and logistic regression, indicate the acceptable accuracy of predicted maps for landslide. The results indicate that ANP methods and logistic regression are accurate in the study of landslide in the area affected by the Arasbaran earthquake. Logistic Regression model had better results. Using these methods together and comparing them with regard to the dependencies of landslip issues can be very useful for identifying areas prone to landslide. As suggested, these methods have acceptable results in analyzing the sensitivity to landslides. The highest density of landslides, even old landslides, are not accidental in the two-axis seismic centers with magnitudes of 6.4 and 6.2 in 2012, indicating a history of seismicity and high tectonic activity in the area. Keywords: Analytical Hierarchy Process, Logistic Regression, Landslide hazard zonation, Arasbaran earthquake, Qoshadagh Fault Zone.

Landslide is one of the destructive erosion events in slopes that causes financial losses on the roads. Identification of factors affecting landslide occurrence and hazard zonation mapping is considered as one of the basic tools to manage and reduce possible damage. In this research, we tried to zonate the landslide hazard at proposed Torghabeh- Darrod road by using Bayesian method, so that we can prevent many human and financial risks. Therefore, by collecting the required information on the landslides distribution in the area, the maps of landslide distribution, along with13 data layers including: slope gradient, aspect, curvature, elevation, landuse, geology, distance from road, distance from drainage, distance from fault, soil and rainfall zones was produced in the GIS environment. By using Bayesian probability theory, the relationships between each factors and landslide points were determined and subsequently the classes weights of each factors were obtained. Finally, landslide hazard zonation maps was prepared by Bayesian theory for the study area, in such a way that all effective factors were used and each factor was deleted. Finally the accuracy and precision of the maps were calculated using the ROC carve and 30% of landslide points. Data show that the precision of prepared probabilistic model by the Eleventh approach modeling (the distance from the fault was removed) was determined about 86.5% (very good) in study area. Using the provided map, we can identify unstable areas, and rely on them in the implementation of development planning particularly road construction.

Landslide is an important geological hazard that causes damage to natural and social environment. Landslide hazard assessment is an important step towards risk management and there are several methods of Landslide Hazard Zonation (LHZ). Landslide susceptibility maps are very helpful to planners and engineers for choosing suitable locations to carry out development. . In this research, we tried to mapping the landslide hazard at Jaghargh watershed by using “weights-of-Evidence” (WOE) method based on GIS technique. Eleven landslide causative factors were considered for the susceptibility analysis. Using landslide location and a spatial database containing information such as topography, lithology, land cover and lineament.The topographic database including information on slope angle, slope aspect, elevation, plan curvature, distance from road and distance from drainage, stream power index and Sediment transport index was developed from a digital elevation model (DEM) .The lithology and the distance from the lineament were derived from the geological database and layers of precipitation areas using meteorological data were obtained. By using weight of evidence method, the relationships between each factors and landslide points were determined and the weights of each factor were obtained. Finally, the accuracy of the map was determined by using Li Index. The results show that the sub affecting height and rainfall factors, respectively with weights 47.66 and 24.47, were identified as the most important factors in a landslide in the study aria. The results of evaluation model accuracy, showed a trend (low to high) in landslide hazard index from low to high risk area,that indicate the model is accurate. Using the provided landslide susceptibility map, we can identify unstable areas, and be used in program development.

Landslide is a natural phenomenon that occurs due to falling down or movement of sediments materials along the hillsides. Velocity and wideness of the phenomenon cause attractive & tragedy one، which can affect on thousands of cubic meter of soil & stone. This phenomenon is the most Common natural earth change phenomena that occur in the geological period which is nowadays known as natural disaster. In present decade its importance is considered to deal with natural disaster and to reduce its risk. One of the important objectives of this research is to identify different parameters in landslides of sensitive area. This project also aims to identify high risk area and low risk area of landslide in order to reduce harmful effects landslide. Therefore، nowadays we need to provide landslide hazard zonation in sensitive areas with unstable hillside because every year it causes huge damages. Therefore، research is necessary for which different methods that give acceptable results by the researchers such as systematic or map query from which statistical method is chosen. In this research، Marbor river basin is zonated by using two variants statistical method which includes value of information and density area In order to prepare the hazard zonation landslide map، it is necessary first to prepare the distribution map of landslide in the region. This is done by field study and using geological maps، topographic maps، digital elevation model (DEM) and land use maps using Ilwis software. The obtained data is then transferred to I1wis software and six information layers such as slope، lithology، rainfall، land use، and distance from roads were used. These layers were then intersected، and classified landslide hazard zonation map with 2 statistical method. This research attempts to zonate the Marbor River domain in the south of Semirom city with an area of 1438 km2 using Binary statistical method because of increasing instability of the slopes and landslide phenomena، to prevent it from human and financial damages. Therefore، by collecting required information on landslides dispersion in the region، landslide dispersion map is provided by preparing six informative layers which includes slope، aspect، lithology، landuse، distance from the road، and precipitation rate. These layers were then intersected، and classified landslide hazard zonation map with Binary statistical method. These methods contain: 1) Information Value This method is based upon the following formula: Wi=Ln (Densclass/Densmap) =Ln (Npix(Si) /Npix (Ni))/ (Npix(Si) / ∑npix (Ni)) where، Wi = the weight given to a certain parameter class (e. g. a rock type، or a slopeclass). Densclas = the landslide density within the parameter class. Densmap = the landslide density within the entire map. Npix (Si) = number of pixels، which contain landslides، in a certain parameter class. Npix (Ni) = total number of pixels in a certain parameter class. 2) Density Area D=Nix (Sxi) /Npix (Xi) W=1000* (D-(∑Npix(Sxi) / ∑Npix (Xi)) Afterwards، we analyzed the accuracy and precision of each method (P، QS). For the analyses and accuracy of the used method، two equations of accuracy and precision are used which are P method and QS method، and to compare the accuracy and precision of the two maps in linear regression and audit analysis، the best method is chosen. These relations are shown: (P) method''s accuracy: Area of land slide in medium and high hazard zone is divided by their area as shown by the correlation: P=KS/S where، P is method''s accuracy in medium and high hazard zones، KS is the landslide area in medium and high hazard zone and S is the related hazard zones area The (QS) method''s Precision is obtained by density ratio as shown in this equation: DR = percent of landslide / percent of area QS=_ (DR-1) 2*Area (%) The result showed the Infomation value is better than other method for this region. 1. Using hazard zonation map of landslide we can design road networks، technical structures، power lines، gas، etc، in sustainable zones and reduce damage 2. Map prepared by Information value method is an appropriate for Landslide hazard zonation in this area 3- The factors studied show maximum of landslide Have occurred in 12-25 slope، South East، Daryan formation، 500-700 rainfall and and with 0-1500 metter distance to road. 4- Avoid building structures near the river. because in this area has high potential for occurrence landslide. 5- East and South East region have minimum potential for occurrence landslide.

Identification of areas susceptible of landslide is one of the primary measures in managing and reducing the damage caused by such phenomena. In this regard, according to the goal and environmental conditions, different zoning models have been proposed. In order to zone and study the areas susceptible of landslides in the north of Neyshaboor, AHP method was applied. Thus, factors causing landslides including slope, lithology, land use, rainfall, the distance from river, the distance from the fault and the distance from the road were analyzed as the main factors for the occurrence of landslide. Layers of information related to each of the factors effective in the occurrence of landslides were prepared by the means of GIS analysis, and effective factors were evaluated using the AHP and paired comparisons. Finally the landslide hazard zonation map was prepared. Considering the final weight of each of the factors, it was concluded that the slope of the landslide and lithology in this area are the first and second most important factors influencing the landslide degree. Also the results of this zoning and the existing distribution of landslides confirmed that places susceptible of landslide in this region are based on landslide areas.

Landslide hazard assessment can be useful information to reduce its losses as well as advance development programs give each region. Landslide hazard map is very useful in estimating، managing and mitigating landslide hazard for a region. One of the areas prone to natural factors in the occurrence of this watershed and from the north country. Therefore was used to AHP models and bivariate statistical methods included landslide index، weight factor، combined with the weight factor and landslide index and combined landslide index model white AHP. AHP method were used to prioritize the factors. Taken to review features showed of lithology، distance from the road and the negative impact of trees along the road construction in forest areas the most important factors in the occurrence of landslide. In the next stage of the models mentioned landslide hazard map was prepared with the four risk categories low، medium، high and very high. It is 40. 67 percent of the high risk areas in the class، 42. 56% of the low risk and 13% in very high. Assessment models in the study area showed that the combined model landslide index (Wi) with the factor weight (Wf) the amount collected qualitative 1. 013 is the best performance and then were Wi model value. /72، Combining methods Wi and AHP value. /625، AHP value. /417 and Wf value. /107.