Landslide hazard zonation using MR and AHP methods and GIS techniques in Langan watershed, Ardabil, Iran

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1 Landslide hazard zonation using MR and AHP methods and GIS techniques in Langan watershed, Ardabil, Iran A. Esmali Ouri 1* S. Amirian 2 1 Assistant Professor, Faculty of Agriculture, University of Mohaghegh Ardabili, Iran 2 M.Sc. Student, Faculty of Agriculture, University of Mohaghegh Ardabili, Iran *Correspondence author, esmaliouri@uma.ac.ir, Tel: (2247), Fax: KEY WORDS: Landslide; MR; AHP; Langan; GIS ABSTRACT Study of landslides triggering factors and zonation of their hazards could help effectively in reducing their damages that this research is an effort in this connection. With studying landslides triggering factors and mass movements hazard zonation methods, Langan watershed was zoned with two Multiple Regression (MR) and Analytical Hierarchy Process (AHP) methods, and the regional model was presented. Methodologically, at first the landslides triggering factors of the area were studied and the important factors with their priority arrangement including lithology, slope, land use, lineament factors (road & river), precipitation, aspect and altitude were investigated. Then their maps with using GIS & RS were supplied and classified. Also the landslides distribution map was supplied using GPS and GIS techniques. In Multiple Regression (MR) method, four factors including lithology, slope, land use and precipitation were used and the map of their homogeny units with using GIS, ILWIS 3.2, was supplied. For quantitation the factors and their weighting were used the landslides percentage in homogenous units and the statistic analysis was the stepwise method, that the zonation model with using this model and omission of precipitation factor was earned. In Analytical Hierarchy Process (AHP) method, at first, the proposed factors for zonation (the seven factors that mentioned in the past) have compared pairly and their weights have determined using analytical hierarchy process. Also, in this paper cumulative curve, has been used because of to classification the factors, and with regard to per class landslides percentage, their values were determined. Finally with regard to earned weights for each factor and their class s values, the zonation model was presented. In analyzing the results and model evaluation, was used the GIS technique, so as the zoned map has been crossed with landslides distribution map and the model accuracy was confirmed with determining the landslides percentage in per zoned class. INTRODUCTION As a final goal, the investigation and studying of landslides is to find ways to reduce their damages. This objective is possible through different ways such as landslides hazard zonation for determining the hazardous areas and providing recipes and regulations for appropriate uses of these areas or for keep away from them. In consequence of heavy rainfalls in 1995 in Germi town, Ardebil province, Iran has occurred a very great landslide in Katulabad quarter and destroyed about 250 residential units and 300 families were been homeless that it evacuated the quarter from habitation in several days. At present about % of Germi town in Langan watershed is in break out of landslide phenomenon. With regard to this huge extension, we can easily realize that non-attention to land capability (no principal civilization) and landslide phenomena are the basic points in this area (Ansari and Blourchi, 1995). Langan watershed was selected for this study that runs from 48º 01' to 48º 08' eastern longitudes and from 38º 54' to 39º 03' northern latitude. This watershed is a part of Ardebil province, Iran, and reaches from north to Barzand Chay watershed, from south to Azerbaijan

2 republic, from east reaches to Arazchay watershed and from west reaches to Sarighamishchay watershed. MATERIALS AND METHODS 1. Landslides triggering factors The common method to study the triggering factors is to use from questionnaire and morphometring the landslides inside the watershed as field works (Abramson, et al, 1995), which this method has been done for Langan watershed. 2. Priority arrangement of the effective factors Since the importance of the factors in landslides occurrence are different, therefore the investigation and correctly priority of the factors are necessary. Therefore with contemplating the landslides area percentage in each factors class, and scattering of each class landslides (scattered or gathered) and with regard to field works, the most important factors in landslides occurrence of Langan watershed were arranged with their priority: 1. formation type 2. slope 3. land use 4. road and river (lineament factors) 5. precipitation 6. slope aspect 7. elevation 3. Providing the landslides distribution map With interpreting the aerial photographs, the sustainable areas for landslides were recognized (method: Nagarajan et al, 1998). But the most important part of providing the landslides distribution maps, has been done in field controls. For determining the minute location of landslides and to provide the landslides distribution map, was used from GPS. 4. Providing the landslides triggering factors maps GIS & RS techniques provided the maps of seven factors that had been recognized as important factors and had been arranged for their priority (Van Westen And Soeters, 1997). Then for the reason of districting the maps, each map was classified into several classes: 1. lithology agent map, 2. slope map, 3. land use map, 4. lineament factors map, 5. precipitation classes map, 6. altitude classes map, 7. slope aspect map 5. Determining the homogeneous units For purpose of zonation Langan watershed with Multiple Regression (MR) method, were selected four variables (factors): lithology, slope, land use and precipitation, and the homogeneous units map of them was supplied. This work was done via overlay menu in Idrisi software circumference and was derived 126 homogeneous units in total. 6. Quantization of the factors and weighting Quantization of the factors and weighting their classes are done with regard to landslides area percentage in the homogeneous units that are similar in the entire factors viewpoint, but one of the factors is variable in its classes (Nagarajan et al, 2000). For doing this work, at first, the homogeneous units map are crossed with landslides distribution map and the area of landslides in each unit are derived. The rate of landslides area to the homogeneous units' area is taken into as a variable Y. That Y will be the function of effective factors. For weighting (rating) of the classes of the factors, was used the linear regression relationship between two classes that have the maximum correlation together.

3 RESULTS Zonation of Langan watershed using Multiple Regression (MR) method - Statistical analysis and creating the multiple regression relationship among the factors After weighting the classes of all factors, these data were moved into spss software circumference and four treatments; slope, lithology, precipitation and land use were reposed versus homogeneous units with number 126 replication. In selecting multiple regression manner, was selected stepwise manner with P > 90%. With regard to the results of multiple regression analysis, the precipitation factor was removed for the reason of having little significance coefficient. But the factors slope, lithology and land use were significant in confidence level between % and the R-value of them was Finally the regional model with using MR method was derived as follow: Y =.977X X X (1) wherey is susceptibility coefficient, X 1 is land use factor, X 2 is lithology factor and X 3 is slope factor. In the end, this model was generalized in all homogeneous units, then the tabular file of its map was moved into Excel software and the curve between Y values and their pixels frequencies was drawn (figure 1). Fig. 1. Pixels frequencies versus Y values in MR method Zonation of Langan watershed using Analytical Hierarchy Process (AHP) In this method, the preference of a factor as compared with the other factor is taken into as a table 1, then these judgments are changed into quantitative values from 1 to 9 (Ghodsipour, 2000). After formation of table1, in this time for the reason of zonation the area, following stages have been done orderly (Ghodsipour, 2000): 1- Pair comparing of the factors and their priority based on their weights For comparing of the factors and determining their preference as compared with each other, was formed table 2. At present for the reason of to calculate each alternative weight of pair comparing matrix, is used from arithmetic mean method. In this method, at first, the values of each column are summed together (that this stage has been done in table 2). Then the values of each alternative (element) of the matrix is divided by the sum value of the same factor column, and in the end stage, the factors mean values is derived in each rows (table 3). These mean values of each row are the weight values for each map.

4 Therefore, the priority of each factors based on earned weights in connection with landslides hazard in the study area was as the under arrangement: 1. Lithology, w 1 = 0.423, 2. Slope, w 2 = 0.216, 3. Land use, w 3 =0.118, 4. Lineament factors, w 4 = 0.118, 5. Precipitation, w 5 = 0.058, 6. Aspect, w 6 = 0.039, 7. Altitude, w 7 = Table 1. The preference of factors and conversion they into quantitative values Preference of a factor as compared with the other Numerical value Extremely preferred 9 Very strongly preferred 7 Strongly preferred 5 Moderately preferred 3 Equally preferred 1 Intervals between preferences 2,4,6,8 Table 2. Pair comparing of the factors in landslide triggering of Langan watershed Lith. Slo. L.U. L.F. Pre. Asp. Alt. Lithology Slope 1/ Land use 1/5 1/ Lineament F. 1/5 1/ Precipitation 1/7 1/4 1/3 1/ Aspect 1/8 1/5 1/4 1/4 1/2 1 2 Altitude 1/9 1/6 1/5 1/5 1/3 1/2 1 Sum Classification of the factors using cumulative curve The method that has considered in this paper, includes the classification of factors into several classes with regard to their changing in the nature. For example, if we want to classify the altitude map, it will be better that we take into the suddenly changes of the area topography in connection with the elevation. These suddenly changes could be recognized through drawing cumulative curves between the factors values (elevation amounts) versus their pixels frequency. This work contains the document file of DEM map that in Idrisi software, we can draw the histogram of it and determine the classes. With using this method, the altitude map was classified into four classes: a) m b) m c) m d) m 3- Valuing to each classes of factors With using landslide area percentage in each class of different factors, all classes were valued from 0 to 100. In this case, the class of each factor that had the maximum percent of landslides area, was contained the maximum value 100 and proportional with that, to each the other classes with regard to their landslides percentage, were given different values (Mohammadkhan, 2001). 4- Presenting the model and zonation of watershed After the valuing of area with regard to seven factors, at present, the values of seven factors classes (m) are multiplied by derived weights for each factor (w 1 w 7 ) and then are summed together. Finally the total value M for each pixel and the regional model will be derived: M = w 1 X 1 + w 2 X 2 + w 3 X 3 + w 4 X 4 + w 5 X 5 + w 6 X 6 + w 7 X 7 (2)

5 Table 3. Arithmetic mean method for calculating the factors weights (Maps weights) Lith. Slo. L.U. L.F. Pre. Asp. Alt. Mean Lithology Slope Land use Lineament F Precipitation Aspect Altitude And with replacing the weights (w 1 w 7 ) that had been earned previously, the final model was derived: M = 0.423X X X X X X X 7 (3) where M is susceptibility coefficient, X 1 X 7 are the lithology, slope, land use, lineament factors, precipitation, slope aspect and altitude factors, respectively, and w 1 w 7 are the weights related to each X 1 X 7 factors. For the reason of separation M values into several susceptibility classes, the cumulative curve between pixels frequency and M values has been drawn (similar to MR method and figure 1). With regard to figure 3, Langan watershed has been divided into five susceptibility classes. Fig. 2. Landslides hazard zonation map of Langan watershed with using MR method Fig. 3. Landslides hazard zonation map of Langan watershed with using AHP method

6 CONCLUSION Table 4 shows that AHP method is suitable in landslides hazard zonation of Langan watershed from susceptibility to landslides viewpoints. Because it shows that with increasing the classes' susceptibility, the landslides area percentages have been increased too, and landslides area percentage of its classes are logical in compare with MR method. One of the AHP method preferences, it is that in this method, the weighting of selected parameters is done very logical and the parameters could be arranged with their priorities. Also valuing of classes is very easy, that could repeat its stages several times until to give a better result. Therefore in this case, the derived model will be better for the reason of existence of more parameter (Hassanzadeh, 2000). As a final result, it is that AHP method is better than MR method for the reason of participating more factors, principal classification and reducing the expert s ideas and its accuracy is very much. Therefore the AHP model was selected as a final model for landslides hazard zonation of Langan watershed. Table 4. Specifications of the area and landslides area percentage in zoned classes of two MR and AHP methods AHP model MR model Susceptibility Area (Pixel) Landslides area Area (Pixel) Landslides area classes percent percent REFERENCES Abramson, Lee. W., Thomas S. Lee, Sunil Sharma, Glenn M. Boyce, 1995, Slope stability & stabilization methods. Ansari, F. and M.J. Blourchi, 1995, The landslides of Germi town, Ardebil province, Geology organization of the country, Iran. Ghodsipour, S. 2000, Analytical Hierarchy process (AHP), Amirkabir University press. Hassanzadeh, N. M., 2000, Landslide hazard zonation in shalmanrood watershed, M. Sc. thesis, Tehran University. Mohammadkhan, S., 2001, Providing a model for landslide hazard zonation (a case study of Taleghan watershed), M.Sc. thesis, Tehran University. Nagarajan, R., A. Mukherjee, A. Roy and M.V. Khire, 1998, Temporal remote sensing data and GIS application in landslide hazard zonation of part of western ghat, India. Int. J. Remote sensing, Vol. 19, No.4, Nagarajan, R., A. Roy, R. Vinodkumar, A. Mukherjee, and M.V. Khire, 2000, Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng. Geol. Env. 58. Van Westen, C.J. and R. Soeters, 1997, GISSIZ: Geographic information system in slope instability zonation, ITC, Netherlands. 156 PP.