Journal of Engineering Geology

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(Paper pages 24-41 ) Different methods are used for landslide hazard zonation. Some of the methods are based on specific condition of the area. In this research, applicapibility of a number of landslide hazard zonation methods in Latian Dam watershed is evaluated. For this purpose Latian Dam watershed due to variety in geological condition, engineering geological characteristics, topography, geomorphology, and precipitation was selected. Different thematic layers including geology, distance from active faults, elevation, slope rate and aspect, precipitation, and vegetation cover were prepared. More than 150 single and landslide zones were recorded based on aerial photo interpretation and field survey. The data were analyzed to find out about landslide controlling factors. Considering instability controlling factors, Nilsen, Information Value, Weight of evidence, and Density area methods were used for preparation of landslide hazard zonation in the watershed. The comparison of the prepared hazard zonation maps with landslide inventory map indicates that weight of evidence and information value methods with accuracy of 99.4 and 99.7 percent respectively are most appropriate methods for preparation of landslide hazaed zonation map in similar area in Central Alborz.

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The Keshvari watershed is located at south east of Khorramabad city in Lorestan province. This area is one part of the folded Zagros zone based on structural geology classification. By consider the type of geological formations, topographic conditions and its area, this watershed is very unstable and capable for occurring landslide. In this study, artificial neural network (ANN) with structure of multi-layer percepteron and Back Propagation learning algorithm used for zonation of landslide risk. The results of ANN showed the final structure of 9-11-1 for zonation of landslide risk in Keshvari watershed. According this zonation, 23.81, 7.53, 6.49, 18.68 and 43.47 percent of area are located in very low, low, moderate, high and very high risk classes, respectively.

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Prediction of location of future earthquakes with event probability is useful in reduction of earthquake hazard. Determination of predicted locations has attracted more attention to design, seismic rehabilitation and reliability of structures in these sites. Many theories were proposed in the prediction of time of occurrence of earthquake. There is not a method for prediction time of future earthquakes. Many studies have been done in the prediction of magnitude of earthquakes, but there are not any investigations on prediction of earthquake hazard zonation. In this study, the locations that have probability of the event of future earthquake have been predicted by artificial neural networks in Qum and Semnan. Neural networks used in this study can extract to complicate properties of patterns by receipting the interval patterns. Furthermore, the map of earthquake hazard zonation has been drawn. Properties of occurred earthquake were collected since 1903. The most probable event of earthquake in Qum has been predicted 31.6% in center, and 28.9% in north of Semnan

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In this research, one of the new methods for seismic landslides hazard zonation (CAMEL) to predict the behavior of these types of landslides have been discussed.  It is also tried to eveluate this method with the proposed Mahdavifar method.  For achieving this result, the influence of  Sarein earthquake (1997), have been selected as a case study. In order to apply seismic hazard zonation, the methodology of Computing with Words (CW), an approach using fuzzy logic systems in which words are used in place of numbers for computing and reasoning is employed. First, the required information which includes disturbance distance, ground strength class, moisture content, shake intensity, slope angle, slope height, soil depth, terrain roughness, and vegetation have been collected using air photos, Landsat Satellite images, geological and topographic maps, and site investigation of the studied region. The data is digitized and weighted using Geological Information System (GIS). At the next step, the hazard rate and areal concentrations with respect to landslide types are calculated using CAMEL program and then, landslides hazard map produced by the above mentioned method is compared with landslides occurred as a result of Sarein earthquake. Finally, for evaluating on prediction of the earthquake-induced landslides, empirical comparison have been done between CAMEL and Mahdavifar methods.

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In the present study, landslides occurred during 1997 Sarein, Iran earthquake are discussed and evaluated. In order to meet the objectives, the Computing with Words (CW), an approach using fuzzy logic systems in which words are used in place of numbers for computing and reasoning is applied. Firstly, the necessary information which include disturbance distance, ground class, moisture, shaking intensity, slope angle, slope height, soil depth, terrain roughness, and land-use have been collected using air photos, LANDSAT satellite images, geological and topographic maps, and site investigation of the studied region. The data is digitized and weighted using ARCGIS software. At the next step, the hazard rate and predicted areal concentrations of landslides with respect to their types are calculated using CAMEL software (Miles & Keefer, 2007). CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using geographical information system(GIS). Finally, landslides hazard map is compared to landslides triggered by Sarein earthquake.

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In present research, landslide hazard zonation of Latian dam watershed area has been carried out using Analytic Hierarchy Process (AHP), Valuing area accumulation, Factor overlay and Information value methods. At first, different maps comprising slope, aspect, altitude, faults, drainage network, access roads, lithology, land use and friction angle maps were prepared digitally using GIS. Afterward affecting factors were evaluated using old landslides. The results of evaluation show that seven parameters are important effective factors on sliding in this area. These parameters were leaded to landslide zonation maps. These maps show that potentially high risk zones point of view landslides are located near the central and western boundaries of the reservoir. Performance of four used classification methods were evaluated and compared. The evaluation results show that Valuing area accumulation and Factor overlay are precise methods for evaluating landslide potential in the study area respectively

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Landslides are natural hazards that make a lot of economical and life losses every year. Landslide hazard zonation maps can help to reduce these damages. Taleghan watershed is one the susceptible basin to landslide that has been studied. In this paper, landslide hazard zonation of the study area is performed at a scale of 1:50,000. To achieve this aim, layers information such as landslides distribution, slope, aspect, geology (lithology), distance from the faults and distance from rivers using artificial neural network-based Radial Basis Function (RBF) and perceptron neural network (MLP), has been studied. Principal of RBF method is similar to perceptron neural network (MLP), which its ability somewhat has been identified up to now and there are several structural differences between these two neural networks. The final results showed that the maps obtained from both methods are acceptable but the MLP method has a higher accuracy than the RBF method.

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./files/site1/files/5Extended_Abstract.pdf Extended Abstract
 (Paper pages 91-114)
Introduction
Due to possibility of occurrence in various natural environments and the variety of natural and artificial factors that affect landslides, landslide has special importance in natural hazards. Depending on the landform, several factors can cause or accelerate the landslide. According to previous researches, Human activities, land morphology, geological setting, slope, aspect, climate conditions, proximity to some watershed features such as rivers and faults are the most important parameters. Landslides occur frequently each year and they can cause heavy losses which compensating some of them requires a lot of money and time.
Assessing landslide related hazards with only limited background information and data is a constant challenge for engineers, geologists, planners, landowners, developers, insurance companies, and government entities.
The landslide occurrence in terms of time and place are not easily predictable, for this reason, Landslide Hazard Zonation (LHZ) or Landslide Susceptibility Zonation (LSZ) maps are used to predict the happening of landslides. A landslide susceptibility map depicts areas likely to have landslides in the future by correlating some of the principal factors that contribute to landslides with the past distribution of slope failures. These maps are basic tools for land-use planning, especially in mountain areas. Landslide susceptibility mapping relies on a rather complex knowledge of slope movements and their controlling factors. The reliability of landslide susceptibility maps mostly depends on the amount and quality of available data, the working scale and the selection of the appropriate methodology of analysis and modeling.
Such maps are obtained by dividing of a region into near-homogeneous domains and weighting them according to the degree of possible hazard of a landslide. There are two ways to do landslide hazard zonation: (i) a qualitative approach that is based on expert knowledge of the target area and portrays susceptibility zoning in descriptive terms; and, (ii) a quantitative approach based on statistical algorithms. In the present study of landslide susceptibility zonation, bivariate statistical methods (information value, density area, LNRF, frequency ratio) were used. In bivariate statistical analysis, each factor map is combined with the landslide distribution map and weighting values based on landslide densities are calculated for each parameter class.
Materials and Methods
The best method for studying landslides, which has long been of interest to researchers, is hazard zonation. In this method due to the affecting factors in landslide occurrence, the study area is classified into areas with low to very high risk. Such zonation could be of great help in regional planning. Different methods have been developed for this purpose. In this research four bivariate statistical methods namely information value, density area, LNRF, and frequency ratio are used to investigate the hazard zonation in Poshtdarband region, Kermanshah province. The study began with the preparation of a landslide inventory map. The instability factors used in this study included geology, land use, normalized difference moisture index (NDMI), slope gradient, aspect, distance from faults, distance from surface water, distance from roads, profile curvature and plan curvature. Landslide area ratio was calculated in classes of effective factors maps and weighted by four bivariate statistical methods. In addition, landslide hazard zonation maps were obtained from algebraic sum of weighted maps with regard to breakpoints of frequency curve. Finally, by using density ratio (D_r) Index through all four methods hazard classes were compared and with the help of quality sum (Q_s) and precision (P) indexes these four methods were compared and evaluated.
Results and Discussion
If the landslide susceptibility analyses are performed effectively, they can help engineers, contractors, land use planners, etc. minimize landslide. In this study, bivariate statistical methods were applied to generate landslide susceptibility maps using the instability factors. The bivariate approach computes the frequency of landslides with respect to each input factor separately, and the final susceptibility map is a simple combination of all the factors irrespective of their relative significance in causing landslides in a particular region.
In table 1 subclasses of instability factors which had the highest value in different methods, are summarized.
The density ratio indexes (D_r), quality sum indices (Q_s) and precision indices (P) were used to compare the methods. By overlaying the landslide inventory map of the study area and landslide hazard zonation maps, quality sum (Q_s) and precision (P) indices introduce a suitable model for the studied region, and density ratio index (D_r) introduces division precision among the zones or hazard classes in each zonation model.
Table1. subclasses of instability factors in different methods which had the
highest value

factor methods	aspect	Slope	distance from surface water	land use	plan curvature	profile curvature	distance from fault	distance from the roads	NDMI
information value	N, NE	>40	>1000	forest	concave	concave	<500	>1000	-0.17_ -0.408
density area	N, NE	>40	>1000	forest	concave	concave	<500	>1000	-0.17_ -0.408
LNRF	SW, S	10-20	>1000	pasture	Convex	convex	<500	>1000	-0.17_ -0.408
frequency ratio	N, NE	>40	>1000	forest	concave	concave	<500	>1000	-0.17_ -0.408

The density ratio for information value method in the very high hazard class is accounted 1.700495. These values for density area, frequency ratio, and LNRF methods are, 3.407827, 3.402257, and 1.694628 respectively.
Method precision (P) values for information value, density area, frequency ratio, and LNRF methods are 0.160826, 0.241024, 0.240672 and 0.16942 respectively.
Conclusion

• Frequency ratio, density area and information value methods showed that forest land use, slope and slope shape factors have the highest impacts on a landslide occurrence.
• The LNRF method showed that geology factors, pasture land use and distance from surface water had the greatest role in landslide making.
• For frequency ratio, information value, and density area methods, the effective factors in landslide are the same, however through the LNRF method, the three factors which have the greatest impact on landslide happening, are generally different from the three other methods.
• The density ratio values show that density area and frequency ratio methods respectively have more accuracy and applicability within all used methods for separating hazard classes in the study area.
• The quality sum (Q_s) results indicate that although there are minor differences, the frequency ratio compared to the density area method was more accurate and more applicable for separating landslide hazard in the Poshtdarband region.
• The calculated results of P index indicated that among the used methods, the density area method with a nuance of the frequency ratio method is the most suitable method for the study area.

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Introduction
The landslides, as a natural hazard, caused to numerous damages in residential area and financial loss. In many cases, we can forecast the occurrence probability of this natural phenomenon with using of detail geological and Geomorphological studies. This seems that one of the most effective parameters in landsliding phenomenon is structural parameters, especially faulting in rocky outcrops. For verifying this hypothesis, the Nargeschal area, as high potential of hazardous area, is selected as case study for investigation on influences of faulting on landslide occurrence probability. Many large composite landslides were happened in 2016 and hence, this area is enumerated an active zone of landsliding. This area with geographic attitude 55° 09' 06" to 55° 27' 21" Eastern Longitude and 36° 54' 23" to 37° 05' 15" Northern Latitude located in south of Azad shahr (in Golestan Provinces) placed in Northeastern of Iran.
Geological studies indicate that this area located in northern limb of Alborz fold belt (as a young fold-thrust belt with 900 km length) which formed in late Alpine orogenic events by convergence Afro-Arabian and Eurasian plates. In this zone, the structures have main NE-SW trends with main active faults such as Khazar and North Alborz faults, as reverse faults with north-ward movements. The remnant part of Paleotethyan rocks (which transported from collision zone toward southern part by low angle thrusts) located between these faults and formed the mountain-plain boundary hills.
Material and Methods
In this research, we investigated on effective parameters in landslide occurrence probability of Nargeschal area with using of remote sensing techniques, GIS environment abilities and complementary field investigations. Therefore, we have prepared the seven data layers of geological and morphological effective parameters which are affected on landslide probabilities. These data layers consist of: lithology of outcropped rocks, faulting condition, topographic slopes categorizes cultivation circumstances, seismicity condition, spring population (ground water condition) and surveyed occurred landslides. Then, the content of each data layer is weighted and classified into five classes in GIS environment. Finally, the content of each pixels in all of 7 layers are algebraically summed and recorded as an attributed table. Hence, the landslide hazard zonation map was prepared by drawing the isopotential surface map on the basis of quantities of attributed table by using of GIS functions in Arc view 3.2 software.
Results and Discussion
The results of this research illustrate that a high risk zone is located in central part of area as an oblique broad-stripe zone with NE-SW trend [6]. This zone is correlatable with high density of fractures zone and high population of springs and earthquake focus and also, taken place in Shemshak formation with shale, marl and siltstone rocky outcrops (upper Triassic- Jurassic in age). 
Also, the results of investigations on influences of structural parameters (especially faulting) in landslide hazard demonstrated that faults are indirectly impressed on this hazard probabilities via formed the high slope topography, poor strength faulted rocks, locating of spring presences and creation of seismicity, and hence, defined the spatial pattern of landslides.
Conclusion
Nargeschal area in Northern limb of Eastern Alborz is selected as case study for investigation on temporal relationship between Faulting and Landslides. The following conclusions were drawn from this research.
- It seems that the fault surface plays the role of rupture planes for landsliding.
- The structural factors also increased the ground slope and then, the close relationship is formed between landslides and faults.
- The results demonstrate the genetically relationships between landslides and faults in macroscopic scale in Nargeschal area.