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\title{Application of a Physically based Model for Terrain Stability Mapping in North of Iran}
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\begin{document}

             \author[1]{Mehrdad  Safaei}

             \author[2]{Husaini  Omar}

             \author[3]{Zenoddin B M  Yousof}

             \affil[1]{  University putra malaysia}

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\date{\small \em Received: 13 December 2012 Accepted: 2 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


In This paper, we studied of the landslide occurred in the northern of Iran in the southern Sari in the road of Kiasar using a physically based model has been named SHALSTAB and  landslide instability mapping of study area is determined.   The Aim of This paper is to determine the Effectiveness of road construction in the landslide occurred. The model runs to base on geotechnical data extracted from laboratory testing on the 15 landslide points. A landslide inventory map along the road Kiasar, including 63 landslide points was prepared for study area. The results of field investigations, analyzing geological data, laboratory tests and running model indicated that 88% of landslides are in the unstable region which represents a successful prediction of running model in this area and also according to this model, 25/9 percent of the areas were located as stable. The results established that the SHALSTAB model is also a valuable model for prediction of landslide prone zone for the occurrence of shallow landslides in study area.

\end{abstract}


\keywords{landslide, road, SHALSTAB, stability mapping}

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\let\tabcellsep& 	 	 		 \par
andslides are one of the main geological problems in study area. Multiple causal environmental factors influence on the occurrences of landslides in along the road. Forested slope of northern a lborz mountainous in north of iran is one of the most hazardous sliding area. In the last few years, the landslide events have been increased due to large-scale and major land-use changes by agricultural and housing development, road and building constrictions in areas susceptible to landslides. The area's most seriously affected by landslide occurrence and caused destruction road accessing and rural settlements.\par
There are many approaches [fig. \hyperref[fig_1]{1}] to assessing slope stability and landslide hazards  {\ref (Sidle et al., 1985;}\hyperref[b9]{Montgomery and Dietrich, 1988;}\hyperref[b8]{Dietrich et al., 1992;}\hyperref[b19]{Sidle, 1992;}\hyperref[b7]{Dietrich et al., 1993;}\hyperref[b11]{Montgomery and Dietrich, 1994;} {\ref Wu and Sidle, 1995;}\hyperref[b13]{Pack, 1995)}. The most widely used include \hyperref[b11]{(Montgomery and Dietrich, 1994}): (A) field inspection using a checklist to identify sites susceptible to landslides ; (B) projection of future patterns of instability from analysis of landslide inventories; (C) multivariate analysis of characterizing observed sites of slope instability; (D) stability ranking based on criteria as slope, litho logy, land form, or geologic structure; and (E) failure probability analysis on slope stability models with stochastic hydrologic simulations. Each of approaches is valuable certain applications  {\ref (Pack et al., 2001}).\par
The geotechnical model, which is deterministic or probabilistic, has been widely employed in civil engineering and engineering geology for slope stability analysis. A deterministic approach was traditionally considered sufficient for both homogenous and nonhomogenous slopes. The index of stability is a wellknown safety factor, based on an appropriate geotechnical model and on the physical mechanical parameters. Calculating the safety factor requires geometrical data, data for the shear strength parameters and information on pore water pressure.\par
Montgomery and Dietrich (1994) developed a physically-based model based upon a combination of the infinite slope equation, and a hydrological component based on steady-state shallow subsurface flow. This model, called SHALSTAB, has been used extensively by researchers within the forestry field in the western US  {\ref (Montgomery et al., 1998)} and in Italy \hyperref[b0]{(Borga et al, 1998)}. Other slope stability models developed by the US Forest Service are the Level I Stability Analysis (LISA) and Stability Index Mapping (SINMAP) which are both based on the infinite slope equation. All the deterministic models were executed using special extension in the spatial analysis in recent types of the Arc GIS software \hyperref[b17]{(Safaei et al., 2010)}    The Digital elevation model (DEM) of the study area shows that the topographic elevation is from 210 to 1976 meters. Furthermore, the weather statistics show that the greatest amount of rainfall was occurred during December with a mean value of 110 mm.    A landslide inventory has been mapped using the landslides that occurred within the area after construction road. The topographic attributes (slope and contributing area) were generated from the DEM with a 10 -10 m grid size for the study while the Characteristics of Soil (thickness, hydraulic conductivity, density, cohesion and friction angle) obtained from field investigations and laboratory testing in 15 landslide points.\par
SHALSTAB(Shallow Landsliding Stability) is a deterministic model for predicting the rainfall shallow landslide, based on topographic control and has been developed since the early 1990s \hyperref[b8]{(Dietrich et al., 1992}\hyperref[b7]{(Dietrich et al., , 1993}\hyperref[b6]{(Dietrich et al., , 1995;;}\hyperref[b11]{Montgomery and Dietrich, 1994)} .\par
SHALSTAB combines a steady-state hydrological model with an infinite slope stability model. The model tested and applied first in United States and then in around the world and the results has been often satisfactory. It performs as an extension to the GIS program Arc View on DEM (digital elevation models). Equation \hyperref[b0]{(1)} shows the main equation to the model to compute for each grid cell as unite mapping. Although it can be solved for the critical rainfall (Qc) required to trigger landslides in the study area, since we did not have much reliable data concerning the spatial variability of soil transmissivity (T), we used the ratio Qc/T, as mentioned by  {\ref Dietrich and Montgomery (1998)}.log ?? ?? = ??????? ??/?? . ? ??? ?w . g. z. cos 2 ?. tan( ? ? ) + ?s ?w . (1 ? tan? tan ? ? )?\textbf{(1)}\par
Where: Qc is the critical rainfall necessary to trigger landslides; T is the soil transmissivity (as a product of soil thickness and saturated hydraulic conductivity); a/b is the contributing area per contour width? ? Is the local slope, ? w is the density of water; g is the acceleration of gravity, z is soil thickness; ?s is soil bulk density, ? is the soil friction angle and ?? Is cohesion. The levels of instability base on Log Q?T -1 classes have been shown in Table  {\ref .}2.  
\section[{Results}]{Results}\par
The Figure \hyperref[b3]{(4)} shows a Geology and landslide distribution of the study area and that the places of highest instability is located in the around the main road of the region.  Contribution area and slope map extracted from DEM map using the model that shown in figures (  {\ref 5}) and \hyperref[b5]{(6)}. The landslide susceptibility of study area based on different stability classes has shown in figure \hyperref[b6]{(7)}.     
\section[{Global Journal of Researches in Engineering}]{Global Journal of Researches in Engineering} 
\section[{Conclusion}]{Conclusion}\par
In order to predict future landslides in the region, landslide susceptibility mapping has prepared in the area. Major part the slopes are covered by vegetation, which mainly consists of alder, hornbeam and maple then the model, is unable to calculate of Root Strength in slope stability. Therefore, this is an important limitation for application to the model in the area. Figure \hyperref[b6]{(7)} is shown different instability classes base on -log (q/T) parameter (Table \hyperref[tab_4]{4}). Approximately, 30\% of the entire slope stability modeling area study area was classified as unconditionally stable.\par
Furthermore, about 10 percent of observed landslides located in the stable zone that indicated error of the model. About 70\% of the area classified as an unstable area that illustrated high-potential landslide hazard. About 16\% of area classified as unconditionally unstable with 15 observed landslides and also on 50\% is shown as a high instability zone with 37 landslide locations and about frequency of 60\%. SHALSTAB instability classes on different lithology have been shown which Miocene information makes up nearly 90\% of the underlying lithology that classified as instable or moderate instability. Therefore, the lithology is a most important intrinsic causal factor in study area.  Overall percentage of landslide points correctly classified up to 88\%. Therefore, the results have shown that even using a small scale (1:50.000), the model is a considerable predictive tool to recognize landslide susceptible zones. Base on results, the model is more accurate in compare with other models for prediction rainfall induced landslide.  
\section[{Global Journal of Researches in Engineering}]{Global Journal of Researches in Engineering}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Introduction}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-8.png}
\caption{\label{fig_7}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-9.png}
\caption{\label{fig_8}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-10.png}
\caption{\label{fig_9}Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8}\includegraphics[]{image-11.png}
\caption{\label{fig_11}Figure 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-12.png}
\caption{\label{fig_12}Figure 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
. The\\
model has evaluated by comparison between landslide\\
predictions by the model with occurrence landslide in\\
the area.\\
Furthermore, this model has been applied\\
successfully by several researchers in different parts\\
around the world (Rafaelli et al, 2001; Csadei et al, 2003;\\
Claessenss et al, 2005; Santini et al, 2009; Cervi et al,\\
2010). The methodology is a couple with a hydrological\\
model and an infinite slope stability model using ARC\\
GIS software.\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3096899224806201\textwidth}P{0.5403100775193799\textwidth}}
Classes SHALSTAB\tabcellsep Interpretation of Class\\
Chronic instability\tabcellsep Unconditionally unstable and unsaturated\\
Log Q?T -1 < -3.1\tabcellsep Unconditionally unstable and saturated\\
-3.1<Log Q?T -1 < -2.8\tabcellsep Unstable and saturated\\
-2.8<Log Q?T -1 < -2.5\tabcellsep Unstable and unsaturated\\
-2.5<Log Q?T -1 < -2.2\tabcellsep Stable and unsaturated\\
Log Q?T -1 > -2.2\tabcellsep Unconditionally stable and unsaturated\\
Stable\tabcellsep Unconditionally stable and saturated\\
\tabcellsep IV.\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.19999999999999998\textwidth}P{0.0611111111111111\textwidth}P{0.18611111111111112\textwidth}P{0.1361111111111111\textwidth}P{0.09999999999999999\textwidth}P{0.11388888888888889\textwidth}P{0.05277777777777778\textwidth}}
\tabcellsep \tabcellsep \multicolumn{3}{l}{SHALSTAB Instability log(q/T ) classes}\tabcellsep \tabcellsep \\
Area\tabcellsep 10\tabcellsep (-2.8--2.5)\tabcellsep (-3.1--2.8)\tabcellsep <-3.1\tabcellsep -10\tabcellsep Total\\
\multicolumn{2}{l}{(unconditionally}\tabcellsep (moderat)\tabcellsep (moderate high\tabcellsep (high\tabcellsep ( unconditionally\tabcellsep \\
\tabcellsep Stable)\tabcellsep \tabcellsep instability)\tabcellsep instability)\tabcellsep Unstable)\tabcellsep \\
Region (km 2 )\tabcellsep 50.7\tabcellsep 3.81\tabcellsep 13.46\tabcellsep 96.5\tabcellsep 31.5\tabcellsep 195.97\\
\% Area\tabcellsep 25.9\tabcellsep 1.9\tabcellsep 6.9\tabcellsep 49.2\tabcellsep 16\tabcellsep 100\\
Number of\tabcellsep 7\tabcellsep 0\tabcellsep 4\tabcellsep 37\tabcellsep 15\tabcellsep 63\\
Landslide\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\%Landslide\tabcellsep 11.2\tabcellsep 0\tabcellsep 6.3\tabcellsep 58.7\tabcellsep 23.8\tabcellsep 100\\
V.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{© 2013 Global Journals Inc. (US) Application of a Physically Based Model for Terrain Stability Mapping in North of Iran} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
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\end{document}
