Journal of Engineering Geology

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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
One of the main problems in the Golestan province watersheds is the high degree of erosion and soil degradation, so that the equilibrium between the soil process and the soil erosion is unbalanced, and the erosion rate increases from west to east. Among these, the gully erosion and piping have the highest role. Gully is a canal or stream with the headcut with active erosion, sharpened slope and steep walls that results from the destruction of surface flow (usually during or after the occurrence of precipitation), dissolution movements, and small mass movements. The extent of gully in the eastern parts of Golestan province has caused the land degradation of arable land and landscape and has increased the conservation cost and etc. Because of connecting upstream areas of the basin to the downstream areas, gully has particular importance, which provides the possibility of sediment and pollutant transport, road destruction and financial losses to agricultural lands. In order to prevent and control the development of gully processes from a small scale to large one, it is a versatile utility to identify and extract the areas prone to gully erosion.
Due to the high intensity of gully erosion and its increasing growth in the Gharnaveh watershed, the Garnaveh River has an unstable status and severe eroded gully, and in some areas it has a great depth and vertical lateral walls, as well. Therefore, in this research, the watershed of Garnaveh was selected to prepare the risk areas of gully erosion.
The aim of this research is to determine Gully Erosion Hazard zoning using Frequency Ratio and Gupta & Joshi methods (Gully Nominal Risk Factor-GNRF) in the Garnaveh watershed (Golestan province). Ultimately, the accuracy of the model has been evaluated using quality sum method and Kappa coefficient.
Material and methods
The study area is located in the northern part of Iran, Golestan province. The Garnaveh watershed with an area of about 78430 hectares lies between longitudes 370360 E and 414472 E, and latitudes of 4183819 N and 4155267 N (UTM Zone 40).
At first, gully erosion inventory map with the scale of 1:75,000 (dependent variable) for the Gharnaveh watershed has been prepared using multiple field surveys and satellite images. From total gullies, 70% have been selected randomly for building gully erosion hazard zoning model and the remaining ones (30%) have been used to validate the provided model.
In this research, seven data layers including slope percent, slope aspect, plan curvature, lithology formation, land use types, distance from rivers and distance from roads have been selected as gully erosion controlling factors (covariates/ independent variables) and then they have been digitized in ArcGIS software. The amount of Gully density of each factor class has been calculated from a combination of independent and dependent variables, and the rating of classes have done based on Frequency Ratio and Gully Nominal Risk Factor equations. Finally, the Gully erosion hazard zoning map has been drawn from the summation of weighting maps in ArcGIS. In this map, the value of each pixel is calculated by summing the weights of all the factors in that pixel. The pixel values are categorized based on the natural breaks classifier into very low, low, medium, high and very high hazard zones. Then, an accuracy of zoning map has been evaluated by quality sum method and Kappa coefficient.
Results and discussion
The result of affecting factors classification of the gullies shows that loess deposits formation, rangeland, areas with low distance from road and rivers, northwest aspect, low slope amplitude and concave slopes contain the most susceptibility to gullying. The results of frequency percent comparison of gullies in hazard classes show that from all gully zones in the validation step of the GNRF and frequency ratio models %74.52 and %78.11 of zones are located in the high and very high risk classes, respectively. The result of model validation using the quality sum method and a Kappa coefficient show that the frequency ratio model is a more appropriate model for gully erosion hazard zoning (with the quality sum and a Kappa coefficient of 3 and 0.89, respectively) than the GNRF model (having the quality sum and Kappa coefficient of 1.27 and 0.74, respectively).
Conclusion
In this research, the areas susceptible to gully erosion in the Gharnaveh watershed have been mapped with the frequency ratio and GNRF (for the first time) models. For this purpose, 7 affecting factors (independent variable) and 805 gully zones (dependent variable) were provided to measure the hazard maps of gully erosion. The following results are obtained from this study.
- The geology factors were identified as the most effective factors in the occurrence of gully erosion in the Gharnaveh watershed.
- Based on the gully erosion zoning hazard map of the Gharnaveh watershed, more than 70 percent of gullies are situated in the very high and high hazard classes.
- The produced gully erosion hazard map is useful for planners and engineers to reorganize the areas susceptible to gully erosion hazard, and offers appropriate methods for hazard reduction and management, as well../files/site1/files/131/4Extended_Abstract.pdf