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Earthquake Induced a Chain Disasters

A strong earthquake not only cause directly damage on constructs but also can result in a series of natural disasters such as landslide, debris flow and flooding. These secondary disasters occurs as a chain disasters as shown in Fig. 1. A strong earthquake can induce a large amount of landslides. And then, a large scale landslide can create a landslide dam when its debris fill into and stop a river. The water impounded by a landslide dam may create a dam reservoir (lake). While the dam is being filled, the surrounding groundwater level rises and causes back-flooding (upstream flooding). And because of its rather loose nature and absence of controlled spillway, a landslide dam is easy to fail catastrophically

2. The effect from the hanging and foot wall of the fault It has been found that the majority of landslides are distributed in the range belonging to the hanging wall of the Yingxiu-Beichuan fault and Pengguan fault, northwest part of earthquake zone. All statistics seem to support that the landslide in hanging wall area is more active than in foot wall areas (Xu et al., 2009a;Yin et al., 2009a). For example, the distribution of large-scale landslides also shows the hanging /foot wall effect. It can be found from the Table 1 that 80 landslides occurred in the hanging wall, 70.8% of the total 113 large-scale landslides, and only 33 landslides occurred in the foot wall, 29.2% of the total number. 3. The effect from the distance to the faults Among the large scale landslides, the two farthest landslides from the fault are about 12.6km in the side of hanging wall and 11.35km in the foot wall. The majority of landslides (about 70%) occured in the region of 3km from the fault. Fig. 3a and 3b show the accumulative percentage of landslide distribution as a function of the distance to the fault in hanging and foot wall respectively. An exponential decay has been found for the number of landslides with the distance to the fault in both hanging and foot wall (Fig. 4) Table 1, although most of large-scale landslides, which many researchers have been studying on, are located in the region of less than 1km from the fault. For example, the Donghekou landslide (No.3 in Table 1) has a distance of 0.3km, the Woqian landslide (No.7 in Table 1) has a distance of 0.2km and Niujuangou landslide (No.14 in Table 1) has a distance of 0.3km from the fault. By examining the positions of the two landslides with the fault zone, it has be found that the two landslides are just located at the locking segment of the fault zone where high stress is believed to be concentrated and a lot of energy was absorbed by the locking of the rupture fault. Therefore, it should be notice that large scale landslides may occur at such kind of locking segment of the fault zone.

Direction effect
By examining the sliding directions of large-scale landslides along Hongshihe valley, it has been found that the directions parallel to or perpendicular to the fault are dominated as shown by the rose diagram in Fig. 5. It is implied that the landslides are controlled by the earthquake wave propagation and the fault movement. The slopes parallel to or perpendicular to the fault are easy collapsed. For rainfall induced landslides, the run-out distances are mostly less than 2 times of the slope height (2H). For example, 95% of the 19,035 landslides induced by rainfall are less than 50m based on the records from 1972 to 2008 in Japan. However, numerous rapid and long run-out landslides occurred during the 2008 Wenchuan Earthquake. Table 2 lists 21 long-run-out landslides with the horizontal distance larger than 500m. These landslides traveled over extraordinarily large distances with extremely high speeds and produced catastrophic results.
For instance, the Wangjiayan landslide (No. 76 in Table 1 and No. 21 in Table 2), occurred at the old town area of Beichuan city, had a run-out distance of 550m. It destroyed hundreds of buildings and resulted in more than 1600 fatalities (Yin et al., 2009a). The Daguangbao landslide (No. 1 in both Table 1 and 2 ) is another long run-out example. The affected area is estimated more than 7.2 km 2 . Its run-out distance is estimated as 4,500m. The most complex in dynamic mechanism is the Donghekou landslide (No. 3 in Table 1 and No. 4 in Table 2 ) which has the run-out distance of over 2.4km. It blocked two rivers and formed two landslide lakes at Donghe village of Qingchuan County. It has been found that the run-out distance is proportional to the area and volume of landslide. The regression formulas of D/H=lgS-3.12 has been obtained with a coefficient of determination of R 2 =0.7681, where D is the run-out distance, H is the slope height and S is the area of landslide. and D/H=0.54lgV-1.26 with a coefficient of determination of R 2 =0.6290, where V is the volume of landslide (see Fig. 6).
Since the mechanism of long run-out landslide is very important in landslide disaster mitigation, it will be discussed in Section 4.  (Xu et al., 2009b). The maximum height of the dam is about 124 m. The maximum capacity of the landslide lake was 2.4×10 8 m 3 , with the length of 20 km (Yin et al., 2009a). Because of its rather loose nature and absence of controlled spillway, it is feared that the landslide dam may fail catastrophically and lead to downstream flooding with high casualties. Hundreds of thousands of residents in downstream Mianyang City were evacuated to the higher locations out of the town before a temporary drainage channel was digged in the dam by Chinese government.

Susceptibility analysis of earthquake induced Landslides
Landslide susceptibility analysis (LSA) is necessary and important for land use planning and disaster mitigation. Many researchers have made great effort to identify the relationships of landslide characteristics such as distribution pattern, type, area coverage and volume with the triggering factors such as the magnitude, intensity and peak ground acceleration (PGA) of the earthquake, coseismic fault rupture (e.g. Lee et al., 2008;Rodriguez et al., 1999;Miles and Keefer, 2009;Keefer, 1984Keefer, , 2000Keefer, , 2002Papadopoulos and Plessa, 2000). Some researchers have studied the relationships of landslide distribution with geoenvironmental factors such as lithology, morphology, presence of secondary active or inactive faults (e.g. Chigira and Yagi, 2006;Jibson et al., 2000;Khazai and Sitar, 2003;Keefer, 2000;Yagi et al., 2009). The Wenchuan earthquake induced landslides has been carried out by several researchers. In this chapter, we show some results from landslide susceptibility analysis carried out in Qingchuan County. Our analysis was based on slope units rather than the traditional grid units. At first, the relationship of landslide distribution with an individual causative factor is analyzed. And then, landslide susceptibility is analyzed by using artificial neural network (ANN) method. Finally, a landslide susceptibility map is made based on the ANN results.

Study area and data source
Qingchuan County is located at the north-western part of the earthquake zone as shown in Fig. 7. The landslides in the area of 3,271km 2 are studied.

Slope unit
Up to now, most of such studies were carried out based on the grid units. There is a problem in grid-based study that a grid may contain different slopes and a large slope may contain several grids with different slope grades. Despite the problem, the grid units were still used just because the slope units are difficult to be indentified for a wide range in the past. Nowadays, it becomes possible and easy to indentify slope units by using GIS-based hydrologic analysis tool (David, 2002), which is based on the watershed divide and drainage lines. The slope unit size should be determined when the tool is used. We suggest that the appropriate slope unit size should match the average size of the landslide bodies in the study area.
A total of 55,899 slope units were indentified in Qingchuan County (Fig. 8). They will be used for landslide susceptible analysis in this study.

Fig. 7. Location of the study area
The basic data include a 1:100,000 geological map and a 10m grid digital elevation model (DEM) made from the available topographic map with 5m contour line interval. 885 landslides were identified from field investigations and RS results.

Relationship between landslide and individual causative factor
More than fifty factors can be considered as the landslide causative factors (Lin, 2003). By considering the data availability, analysis effectiveness, and independence of each factor (Lee et al., 2008), we selected the follow 8 factors: slope gradient, elevation, slope range, slope aspect, specific catchment area, lithology, distance to the fault and distance to the stream, to examine the causative factors contributing to the initiation of landslides. Each causative factor was classified into several categories. The number of the slope units in each category is calculated and the percentage of the category among the whole slopes is given in Fig.9(b). For each category, the percentage of the failure slopes among the slopes in the same category is given as the landslide frequency in Fig. 9(a).

Fig. 8. Slope unit division of Qingchuan area
From the statistical analysis, the following results can be found. 1. More than 90% of the slopes have the slope gradient larger than 20 o . The landslides occurred majorly in the slopes with gradients between 20 o to 35 o . 2. The landslides occurred majorly in the area with the elevations less than 1,200m. 3. The landslides occurred majorly in the slopes with slope ranges from 200 to 400m. 4. There is no clear relationship between landslides and specific catchment area. 5. The number of the landslides in the slopes in N direction is as twice as the slopes in the other directions. 6. The number of the landslides in the slopes with the distances to the fault less than 0.5km is as twice as the slopes in other categories. 7. The number of the landslides in the slopes with the distances to a stream less than 5km is as 3 times as the slopes in other categories. 8. There is no clear relationship between landslides and lithology.

Landslide susceptibility analysis using artificial neural network
The landslide susceptibility analysis is carried out by using artificial neural network (ANN) based on the above statistical analysis results. ANN program is a "computational mechanism able to acquire, represent, and compute a mapping from one multivariate space of information to another, given a set of data representing that mapping, which is independent of statistical distribution of the data, can resolve the nonlinear problem and get high prediction accuracy for classification problem especially for large amount samples (Garrett, 1994). The applications of ANN to landslide susceptibility evaluation have been made by many researchers (e.g. Ermini,L., et al., 2005; S. Lee et al.,2006;Pradhan.B et al.,2010). Nefesilioglu et al. (2008) showed that ANN could give a more optimistic evaluation of landslide susceptibility than logistic regression analysis. Ermini et al. (2005) compared two neural architectures: probabilistic neural network and multi-layered perceptor, and obtained a better prediction result. In this study, the neural network tool SPSS clementine is used since very few parameters are required. One group of the total slopes are randomly selected for training. 611 collapsed slopes of 885 landslides (70%) and 3300 of 55014 un-collapsed slopes (6%) are randomly selected for this group.
Two cases have been analyzed. Case 1 used the 8 factors mentioned as the statistical analysis and Case 2 used the 5 factors, 3 factors with the smaller weight values ware removed from the 8 factors. Also, different layers are used for the two cases. The weights for each factor, experiments structures and the accuracy from the analysis results are shown in Table 3. It can be seen that the accuracy of Case 2 is a little bit higher than Case 1. Therefore, the ANN model from Case 2 is used for the landslide susceptibility classification. Fig. 9. Landslide frequency and slope unit numbers (%) for each category of causative factor The output landslide susceptibility indices (LSI) were converted to GIS grid data in three susceptible levels as shown in Table 4 The landslide susceptibility map is made from the ANN results (Fig. 10). The high susceptible zone occupies 7.42%, the low susceptible zone 86.53% of the whole area. In addition, 6.05% of the area is gray zone.

Conclusions
Landslide susceptible analysis has been carried out in Qingchuan County. 55,899 slope units have been extracted and used for the analysis. The relationship between landslide distribution and the individual causative factor has been investigated by statistical analysis. The clear relationship can be identified for slope gradient, elevation, slope rang, the distances to the fault and the distances to a stream. The ANN analysis also showed the same results, that is, slope gradient, elevation, slope range, distance to the fault and distance to a stream have relatively larger weight. By removing the other three factors with smaller weights, the ANN analysis accuracy got improved. By comparing landslide occurrence locations with susceptibility zones, it has been shown that 99% of landslides can be predicated by the obtained ANN model, but 78.8% of predictions would be false. On the other hand, 99.4% of stable predications are correct and less than 0.2% landslides would not be alarmed. In addition, the gray zone occupies 6% of the whole area. Therefore, the landslide susceptibility classification presented in this study is acceptable.

Analysis of Long run-out mechanism based on trampoline effect
The estimation of the movement behaviour of a potential landslide is very important in order to mitigate the landslide disaster. Especially, the run-out distance is one of the major parameters in landslide risk assessment and preventive measure design. Long run-out is one of the major characteristics of earthquake induced landslides. However its mechanism has not been understood very well. Many researchers have made great effort to understand how and why large falling masses of rock can move unusually long run-out distance. Researchers have repeatedly revisited the problem using a wide variety of approaches. These efforts have yielded no less than 20 mechanical models for explaining long run-out in high-volume rapid landslides. Shaller and Shaller (1996) made a detail summary of the existed models and divided these models into four categories (1) bulk fluidization and flow of landslide debris; (2) special forms of lubrication along the base of the slides; (3) mass-loss mechanisms coupled with normal frictional sliding; and (4) individual-case mechanisms. Actually, most of the existed models are helpful in the estimation of the run-out distance. However, very less of them considered the earthquake dynamical behaviour. For this reason, in this study, we take into account the so-called trampoline effect of earthquake on landslides and propose a multiplex acceleration model (MAM) to explain the long run-out mechanism. Since the MAM model can be easily incorporated into numerical methods, it can be applied to simulate the long run-out landslide very well.

Multiplex acceleration model
For an earthquake induced landslide, the following effects on the movements of the falling stones from the landslide can be considered: (1) a falling stone can obtain kinetic energy from the colliding with the vibrating slope during earthquake; (2) the force of friction between a falling stone and the slope can decrease since the normal force varies with the contact condition during earthquake; (3) The flying and rotation movement of a falling stone may occur much easily in earthquake induced landslides. In order to consider these effects, we divide a period of wave is divided into two phases: Pphase and N-phase as shown in Fig. 11. The P-phase is defined as the period when the slope is moving in the outer normal direction of the slope surface. The slope is pushing the falling stones on its surface and lets them obtain kinetic energy in the P-phase. The N-phase is defined as the period when the slope is moving in the inner normal direction of the slope surface. Since the normal force will decrease when the slope surface moves apart from the falling stones, the force of friction will get decreased in the P-phase.

Fig. 11. P-phase and N-phase definition in MAM
By the repeated exchange of two phases during an earthquake, the falling stone get multiplex accelerated. The MAM model can be seen more clearly by apparent friction angle analysis.
Supposing that a stone with mass m moves from position A to position B during a landslide without earthquake (see Case 1 in Fig. 12), the potential energy decreases by mgh. Based on the energy conservation law, it is easy to obtain the following equation for a falling stone movement in the case without earthquake.
The first term here is for potential energy and the second term is for the work of friction force between the slope and the falling stone, where the sliding movement is considered and the whole curve path is divided into finite linear segments. And m = mass, g = gravity acceleration, h = the falling height, l = the segment length, θ is the segment slope angle, φ is the friction angle, k is the coefficient of conveying from static to dynamic friction and i is the index of segment.

Fig. 12. Apparent friction angle
The apparent friction angle, usually used for the discussion of run-out distance, can be obtained from Eq. (1) as follows When we consider the effects of slope vibration due to earthquake, the kinetic energy of falling stone obtained from the collision with the vibrating slope and the movement patterns (sliding, rolling and flying) should be considered. Thus, Eq. (1) becomes The second term here is for the kinetic energy of a falling stone obtained from the collision with the vibrating slope and v ej is the velocity obtained in j th P-phase and can be expressed as follows is the acceleration of slope vibration due to earthquake, VTR is called the velocity transmission ratio due to collision. The apparent friction angle for the case 2 in Fig. 12 (2), it can be seen clearly that the mechanism of long run-out distance is as follows.
1. The kinetic energy of a falling stone obtained from the collision with the vibrating slope may result in long run-out distance from the second term of Eq. (5). 2. The coefficient of conveying from static to dynamic friction k* in Eq. (5) can be smaller than k in Eq.
(3) because of the N-phase effect, air cushion effect, movement pattern.

Colliding effect
In P-phase, a falling stone can obtain kinetic energy from the colliding with the vibrating slope. According to elastic collision theory, when two objects with different masses collide with each other, the object with smaller mass could obtain larger velocity. Since the mass of a slope is much larger than the mass of a falling stone, the velocity of the falling stone can be much larger than the vibrating velocity of the slope. That is to say the VTR in Eq. (4) can be larger than 1.0. The VTR can be examined by the simple model shown in Fig. 13(a) and (b). The masses of the two blocks are m 1 and m 2 respectively. Before the colliding, the block 1 has initial velocity V 10 toward block 2 which is standstill, i.e. V 20 =0. The friction between blocks and the base is negligible. After the colliding, the velocity of block 1 becomes V 11 while block 2 obtains a velocity V 21 . According to the principles of the conservation of both energy and momentum, we have the following equations.  mV mV mV mV (7) By solving Eqs. (6) and (7), we can obtain the VTR for the case of V 20 =0 as follows It can be seen from the analytical solution Eq. (8) that if m 1 is much larger than m 2 , VTR is to approach to 2.0. Therefore, since the mass of a slope is far larger than the falling stone, the velocity of the falling stone obtained from the slope vibration will be two times of that of the slope vibration velocity during earthquake. The results of VTR given in Eq. (8) have been verified by DDA simulation. The model shown in Fig. 13 is used in DDA simulations. The block one with mass of m 1 has the initial velocity of 10 m/s and the block two with mass of m 2 is at a standstill. After the block one impacted the block two, the velocities of both blocks changed. The block two obtained the velocity from block one. The VTR is calculated from the ratio of V 21 to V 10 . The results obtained from DDA simulations by changing m 1 are shown in Fig. 13(c), together with the theoretical analytical values. The line is calculated from the analytical solution Eq.(8) and the dots are obtained from DDA simulations. It can be seen that the VTR obtained from DDA is in quite good agreement with the analytical solution. However, by close examination, it can be found that the VTR values from DDA are little smaller than the analytical values when the mass ratio of m 1 to m 2 is larger than 4.0. This is because elastic strains of the two blocks are led to energy transformed into potential energy of deformation by the collision in DDA simulation while no strain is considered in analytical solution. Furthermore, when the block 2 has an initial velocity toward block 1, the VTR could become the larger and larger. Fig. 14 shows the results from DDA simulation. This may happen when a stone fall down to the slope in a P-phase, it will get larger rebounding velocity. That means, a trampoline effect can be produced by strong earthquake.

Model tests by shaking table
Model tests using shaking table were carried out in order to investigate the effects of earthquake on the movement of debris. The model slope has the height of 180cm and the slope angle can be adjusted from 30° to 35° as shown in Fig. 15. The movements of 4 kinds of stones with different shapes have been investigated under the earthquake conditions of 0, 200gal and 400gal sine wave of 3Hz. More than 10 times of repeated experiments have been carried out for each case. The following results have been obtained. 1. The movement distance for the case of a 400gal earthquake is 3.4 times longer than the case of no earthquake for the No.4 stone. Therefore, the effect of earthquake on the movement distance is very large. 2. The movement distance for the case of a 400gal earthquake is longer than the case of a 200gal earthquake. So it seems that the movement distance is proportional to the earthquake magnitude. 3. The shape of the falling stone has effect on the movement distance. The movement distance of the No. 5 stone is much smaller than that of No. 4. This is because that the earthquake can change the movement pattern and cause the rotation motion. It can be seen that the No. 5 stone has very sharp edges and vertices, which may stop its rotation movement (Fig. 16). It should be noticed that it is difficult to distinguish the velocity obtained from P-phase because the model slope is too small and there are very few P-phase during the movements.

Numerical simulation of landslide by using DDA
Simulation of landslide by using numerical methods is an effective way in order to overcome the dimension limit of the model test by using shaking table. In this study, Discontinue Deformation Analysis (DDA), developed by Shi and Goodman (Shi et al., 1984), is used since it is applicable to simulating the rigid body movements and large deformations of a rock block system under general loading and boundary conditions. Several extensions of the original DDA have been made in this study so that earthquake wave can be taken into the simulation for different ways. Before simulating a real landslide, the applicability of the extended DDA has been verified by various simple models with theoretic solutions. For example, a simple model shown in Fig. 17 is calculated by both the theoretical solution and DDA simulation. The theoretical solution of movement distance can be calculated by the following formula: www.intechopen.com

Earthquake Research and Analysis -Statistical Studies, Observations and Planning
The results of the movement distance are in good agreement with each other as shown in Fig. 18. We applied the extended DDA to simulate the Dongheko landslide in Qingchuan prefecture. The vertical section shown in Fig. 19 is taken along the red line in Fig. 19. The DDA software and the model are shown in Fig. 20. The parameters for both the material and DDA program are also shown in Fig. 20.
Since the real earthquake curves are not available, a sine wave is used. The movements of debris at different times obtained from DDA simulation are shown in Fig. 21.

Fig. 19. Vertical section of the Donghekou landslide
It has been shown that an 800gal sine wave can cause long distance movements of debris like real one. The rotation and flying movements are the major reasons for long-distance movement, which can be easily observed in DDA simulations.

Debris flow arising from the earthquake
Strong earthquakes not only trigger co-seismic landslides but also they affect subsequent rainfall-induced debris flows over a long term because these co-seismic landslides greatly increased the amount of sediment material for potential debris flows (Lin et al.,2006;Tang et al.,2009;Khattak et al.,2010). After the 2008 Wenchuan Earthquake, the earthquake affected areas experienced two rainy seasons till 2010, and a large number of debris flows occurred, which claimed as many as 450 fatalities. It makes the restoration and reconstruction much more difficult (Xie et al.,2008). In this section, at first, the characteristics of debris flows in the earthquake affected areas are summarized. And then, an approach of simulating debris flow is proposed for disaster mitigation. Finally, a large scale debris flow is simulated so as to show the effectiveness of the proposed.

Clear relation to the earthquake
A large number of debris flows occurred in the earthquake affected areas. For example, there are 46 debris flows found in Beichuan area. The distribution of these debris flows is shown in Fig. 22 in which the red line indicates the main fault of the quake, blue lines indicate rivers and the numbers indicate the locations of the debris flows. It can be seen that these debris flows are distributed along the rivers on the two sides of the earthquake fault. Therefore, the debris flows are highly related to the earthquake.

Large surge peak discharge and huge volume
Since the material sources of debris flow got much richer after earthquake, it is easy to form large scale debris flow. For example, the surge peak discharge reached 260 m 3 /s in the debris flow occurred in Beichuan town on Sept. 24, 2008. The volume was too large to a basin with the area of 1.54km 2 . The cover of debris is so thick that it buried the fourth floor of some buildings. Another example is Sanyanyu debris flow. The volume of the debris reached 144.20 million m 3 . The debris flow carried many huge stones and destroyed houses and bridges (Tang et al., 2009). The critical precipitation for triggering debris flow got decreased obviously after the earthquake. For example, the critical precipitation of 37mm became lower after the earthquake in Zhouqu County areas. A 22mm rainfall could trigger a debris flow during the past 3 years. According to preliminary analysis by Tang et al. (2009), the critical cumulative precipitation has been reduced about 14.8%-22.1%, the critical rainfall intensity per hour about 25.4 %～31.6% in Beichuan County area.

River blocking
The disasters chain induced by the earthquake is very significant. The earthquake induced landslides caused debris flows which blocked rivers, and flooding disasters occurred. For example, Jianjiang River was blocked at 3 locations and half blocked at 8 locations by debris flows during the rainstorm on Sept. 24, 2008. Mianyuan River was blocked at 2 locations and half blocked at 11 locations by debris flows occurred on Aug. 13, 2010. Ming River was blocked at 1 location and half blocked at 5 locations by debris flows occurred on Aug. 14, 2010 (see Fig. 23 (b)).

An approach of simulating debris flow
Many studies on debris flow focused on estimation of an alluvial fan for predicting debris flow inundation areas (Glade,2005;Berti and Simoni,2007). They can be divided into three categories: dynamic models, volume-based models and topographic based models. For the 2008 Wenchuan earthquake, an empirical formula of estimating alluvial fan has been presented by Tang et al. (2010) based on statistical analysis. In this study, we propose an approach for numerical simulation of debris flow to analyze or predict the peak discharge and the volume of a debris flow and its inundation area. The approach consists of the following 5 procedures.

Identify the earthquake induced landslides
The earthquake induced landslides can be identified by RS technique using aerial photos and satellite imagines. The locations and the shapes of all the debris deposits in a drainage area should be obtained from this procedure.

Make field investigations.
The thicknesses of all the debris deposits and the geological and geotechnical behaviors should be investigated in this procedure.
3. Generate the grids using GIS.
Grids are required for solving equations with finite different method. A DEM map can be converted to a raster image using GIS for the drainage area. The grids can be obtained by saving the raster data.
The debris and water mixture is assumed to be a uniform continuous, incompressible, unsteady Newtonian fluid. The following Navier-Stokes equations are used for debris flow governing equations: where (,, )  uu v w is velocity;  is the mass density; p is the pressure;  is dynamic viscosity; (0,0, )  gg , g is the gravitational constant and t is time. The so-called depth-averaged model as shown in Fig. 24  v and w v are the volumetric concentrations of solids particles and water in the mixture; and tan is the dynamic friction coefficient.

Visualize the results
The results from FDM are converted to GIS layers. The maps of maximum surge peak discharge, velocity distribution and in inundation area can be made by GIS. Also the animation of debris flow can be easily made.

Numerical simulation of the Hongchungou debris flow
The proposed approach has been used to simulate the Hongchungou debris flow occurred in Hongchungou drainage area on August 14, 2010. The materials carried by the debris flow blocked Ming River just at a little upper side about 200m from Yingxiu town, the epicenter of the 2008 Wenchuan Earthquake (Fig. 25), . The road along the river became the new temporary river channel and water flowed into Yingxiu town. As the result, serious flood occurred in the newly reconstructed town. The disaster claimed 13 lives and 59 missing persons. The distribution of the earthquake induced landslides has been identified by using RS with the aerial photographs taken by The Ministry of Land and Resources of China. The aerial photograph 0.3m resolution of Hongchungou area is shown in Fig. 26(a). In this study, the object-based analysis (OBA) is adapted for imagine analysis unlike traditional spectral information based image analysis method since there exists the so-called 'salt and pepper' appearance in the output of the latter (Tapas R. Martha et al., 2010). The analysis includes the following steps. 1. The aerial photos are ortho-rectified based on the 20m DEM obtained from the China Geology Survey Bureau, in order to remove the distorting effects of tilt and terrain relief. 2. The image is divided into objects based on homogeneity of pixel values through edgebased segmentation algorithm (Kerle et al., 2009), since it is very fast and only one parameter is needed for scale level. Scale level 30 and merge level 93 are used for image segmentation. 3. NDVI index is used to separate vegetation from other objects. Spatial, spectral and texture attributes are separately computed for each object. Then, various land covers are classifies based on user-defined training data, selected by combing with 3 dimensional image in order to improve the interpretation refinement; 4. The non-landslide objects are eliminated by the assumption that landslide will not occur for the slope gradient less than 5°.  The obtained landslides are shown in Fig. 26(b). It can be seen that all the landslides were accurately recognized. There is not so-called 'salt and pepper' appearance.
Combining with field investigation, we finally selected four landslides: H1, H2, H3, H4 as the main loose source material of the debris flow (Fig. 27). The area of each landslide is 7,688 m 2 for H1, 5,137 m 2 for H2, 2,002 m 2 for H3, 4 4,567 m 2 for H4. Since only a 20m DEM map is available for generating grids, the 4,000mX3,400m area is divided into 200X170 grids by using GIS. The rheological parameters are assumed constant (Tang et al., 2011a), and they are:  d =2050kg/m 3 ,  =1.25,  =1.0,  =0.11, g = 9.8m/s 2 , tan =0.6. The results from FDM are converted to GIS layers for visualizing. The movements of the debris flow are illustrated in Fig. 28(a)-(d) for different time. It can be seen that the river is blocked in Fig. 28(d). The distribution of the maximum depth of the whole flow is shown in Fig. 28(e). According to the simulation results, the debris flow takes 150s to travel about 3,300 m along the valley with an average flow velocity of about 22m/s.
Comparing the simulated results with the actual event, we found that they are in good agreement with each other. Therefore, the proposed approach has been shown applicable and useful for predicting the movement of potential debris flow arising from earthquake.

Conclusions
A strong earthquake can induce a chain of disasters. The disaster chain from the 2008 Wenchuan earthquake has been discussed. The characteristics of the earthquake induced landslides have been summarized as follows. The study of earthquakes plays a key role in order to minimize human and material losses when they inevitably occur. Chapters in this book will be devoted to various aspects of earthquake research and analysis.
The different sections present in the book span from statistical seismology studies, the latest techniques and advances on earthquake precursors and forecasting, as well as, new methods for early detection, data acquisition and interpretation. The topics are tackled from theoretical advances to practical applications.