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Early
warninig systems in landslide occurrences using modelling the landslide flow
paths

 

S. Nishanthan

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Civil Engineering Department, University
Of Moratuwa, Katubedda

[email protected]

Prof. S. A. S. Kulathilaka

Civil Engineering Department, University Of Moratuwa,
Katubedda

 

Abstract: Landslide
is one
of the most frequent and catastrophic geologic hazards. Mostly landslide is
triggered due to extremely high rainfall events. This research is carried out mainly about
modelling the landslide flow paths during after triggering landslides. During these landslides, it caused
fatalities, loss of life and properties. So this research is significance to
avoid this kind of destructions and this research will be acted as an early
warning system to avoid these hazards & losses. The landslide flow path of
known landslide occurrence area is modelled based on using GIS software,
TRIGRS, and DAN 3D software to validate that flow path with original landslide
flow path. The landslide zones are found out from using the GIS software and
the initiation zone of landslide is obtained by using the TRIGRS software.
Thereafter DAN3D software is used to analyse the landslide flow path. It is necessary to
select a susceptibility model with high prediction capability which depends on
the methods used. Because of spatial prediction of landslides is considered to
be useful for land-use planning and the ?rst important step in landslide hazard
and risk assessment. Finally
early warning system will be proposed using these results with respect to rainfall
precipitation data.

Keywords:  GIS
software, TRIGRS software, DAN3D, Debris flow, Landslide flow path,
Precipitation

 

1.   
 Introduction

Land slide is defined as the
movement of a mass of a rock, debris or mass of failed soil and other matter
down a slope which are affected by gravity. It is one of the most frequent and
catastrophic geologic hazards and it causes much destruction in its flow path
causing fatalities, loss of life, property and severe economic losses (Mahalingam
& Olsen, 2016). Due to extremely high
rainfall events, landslide is triggered. 80% landslides were triggered due to
the excessive rainfall
events. This has been witnessed in number of landslides such as Meeriyabedda
landslide at Badulla was caused by the extremely high rainfall events. Another
witnessed is preliminary landslide inventory from Badulla is prepared by
Badulla meterological station based on special reports. Such are 25 landslide
days between 1991 and 2015, 5 landslide days in 2014, and 17 landslides days in
2015. Research
is carried out mainly about modelling the landslide flow paths during after
triggering landslides. During these landslides, it caused fatalities, loss of
life and properties. So this research is significance to avoid this kind of
destructions and this research will be acted as an early warning system to
avoid these hazards & losses. There was no any landslide flow path
modelling with respect to rainfall factor. Scope of this study is build-up a
relationship between rainfall precipitation and the landslide flow path.
From this results an early warning
systems will be proposed.

 

Figure 1: Landslide analyse area

 

2.1  Application of GIS Software

GIS software is used to access landslide inventory
map and extract the particular landslide area from total area of study zone.
During GIS based landslide susceptibility modelling various sophisticated
machine learning techniques have been suggested by the previous researchers. The
performances of the model are differed compare with other models. The
prediction capability of a susceptibility model depends on those models. So
already a research was conducted for investigation and comparison on some
advanced machine learning methods such as Kernel logistic regression,
Naïve-Bayes tree model and Alternating decision tree model for landslide
susceptibility mapping. In this research 12 landslide conditioning factors were
considered which influence on models during the modelling such as distance to
roads, distance to river, distance to faults, altitude, land use, lithological
unit, mean annual precipitation, profile curvature, plan curvature, slope
angle, slope aspect and NDVI. Those data sets were collected from the National
Aeronautics and Space Administration (NASA) website (http://reverb.echo.nasa.gov/reverb/).
 Slope aspect, slope angle, altitude,
pro?le curvature, and plan curvature were extracted using ArcGIS 10.0 software
from a digital elevation model (DEM) which model was extracted from the ASTER
GDEM data. Distance to rivers, distance to roads, and distance to faults were obtained
from topographic map (1:50,000), and geological map (1:500,000) respectively.
Mean annual precipitation map was produced by assembling data of the particular
area. Frequency ratios of each landslide conditioning factor were calculated
using the frequency ratio model and those ratios were normalized in the range
0f 0-1. Thereafter those normalized frequency ratios were used as inputs to
build a model and produce the landslide susceptibility maps. Subsequently, models
were explored that what contributions to landslide susceptibility models by different
condition factors. (Wei
Chen, GIS-based landslide susceptibility modelling: a comparative assessment
of kernel logistic regression, Na€ ?ve-Bayes tree, and alternating decision
tree models, 2017)

Figure 2: Contribution to landslide susceptibility models by different
condition factors

Distance to roads, distance to river, altitude and
land use have a highest importance to the three models and slope aspect, plan
curvature and profile curvature have low predictive capabilities. But from this
12 condition factors, mean annual precipitation is very important factor to
trigger the landslides. Because 80% landslide have been triggered by the
rainfall precipitation in Sri Lanka. The contribution of distance to roads occupies
the highest percentages of 17.667%, 19.846%, and 18.945% for KLR, NBTree, and
ADTree models, respectively. 80.4% of landslide locations occurred due to contribution
of distance to roads along the main valleys and road networks (less than 500 m).
Therefore, we can come to conclude from this that landslide conditioning
factors tend to have different contributions depending on the types of models
used. The researcher has come to conclude that the KLR model is better model, considering
the overall performance and the model construction from this paper. Because of
KLR model has the highest degree of goodness-of-?ts on both training and
validation dataset. The KLR model shows 84.5% stable classi?cation ability for
the training dataset. But NBTree model has the highest goodness-of-?ts (91.4%)
for the validation dataset and not for training model.   Therefore
KLR model was selected as a best model for the highest degree of
goodness-of-?ts on both training and validation process to produce the landslide
susceptibility maps for land-use planner to choose suitable construction sites.
(Wei
Chen, GIS-based landslide susceptibility modelling: a comparative assessment
of kernel logistic regression, Na€ ?ve-Bayes tree, and alternating decision
tree models, 2017)

KLR model is based on a kernel version of logistic
regression. It is carried out as follows:

Where “w” is
a landslide conditioning factors, (.) is a
nonlinear transformation to each input variable, and b is a vector of constant.
A logit function can be written as follows:

 

Then the
transformation equation was detrived.

 

Then they
defined the inner product between the images of vectors as the kernel function.
That is;

Several kinds
of kernel functions can be used. But in this case radial basis function kernel
is applied. It is the most commonly used in practical applications which is as
follows;

 

Where  is the turning parameter that controls the
sensitivity of the Kernel. An optimal “w” that can be found by minimizing a cost function according to the
representer theorem (Kimeldorf & Wahba 1971; Scholkopf et al. 2001). These
are used equations in this research for KLR modelling. (Wei
Chen, GIS-based landslide susceptibility modelling: a comparative assessment
of kernel logistic regression, Na€ ?ve-Bayes tree, and alternating decision
tree models, 2017)

 

2.2 Application of TRIGRS software

The TRIGRS (Transient Rainfall Infiltration and Grid-Based
Regional Slope-Stability Model) software is used for
find-out the initiation zone of the landslide. The TRIGRS software is used for
conjunction with GIS software which is used to prepare inputs data for
inspecting different combinations of geotechnical parameters. TRIGRS has
simulated the dynamic hydraulic conditions within slopes triggered by rainfall
precipitations. In the
TRIGRS model for rainfall-induced landslides research (Viet,
2017)
they applied the TRIGRS model and Scoops3D for the 3D prediction of potential
landslides. Combination
of TRIGRS and Scoops3D software approach is an effective tool for 3D, spatially
distributed and time-dependent assessment of rainfall-induced landslides.
Further developments of mapping of the orthophotographs contribute to determine
the actual size and shape of each landslide and help
to provide a detailed evaluation of the sizes of landslide-triggering areas.
This approach was explored and results were compared with the landslide
initiation locations with respect to the rainfall events on landslide event in
July 2011 at Mount Umyeon, South Korea. In Three-dimensional, time-dependent modelling of
rainfall-induced landslides over a digital landscape research the
soil depth was analysed by using three approaches method such as stability
maps, obtained by the 1D (TRIGRS only) and 3D (TRIGRS and Scoops3D)
time-dependent approaches. (Viet,
2017)

Figure 3: Initiation area of landslide

Almost 1D
& 2D slope stability analyses provides results that suggest lower stability
than 3D analyses methods (Chakraborty
& Goswami, 2016). When modelling the actual mechanism of
landslides, if we use 1D or 2D approaches to that modelling then that model may
fail compare with original one (Bromhead
, 2002).
That is not all we can not consider the direction of the slip surface in 1D OR
2D modelling. That sliding surface is forced to move in assumed downward
direction (Bromhead
, 2002).
So 3D analyse methods are followed.  In
this research they came to conclude that combined TRIGRS and Scoops3D approach
(3D model) is an effective tool for 3D, spatially distributed and
time-dependent assessment of rainfall induced landslides. Because of that most
of landslides are predicted by appropriate time of landslide triggering effect
due to the rainfall intensity. First pore water pressure, initial ground water
table and spatially distributed soil depth were calculated using 1D model
TRIGRS. Thereafter Scoops3D model was executed to measure the subsequent 3D
slope stability using previous calculated value of pore water pressure value.  

Figure 4: Time  dependence  of 
the  percent  of 
unstable  area  predicted 
by  the  1D 
and  3D

 

2.3 Application
of DAN3D Software

The areas of landslide run-out is estimated by DAN3D
which is suitable software for dynamic analysis of rapid flow slides, debris
flows and avalanches carry through numerical model developed by McDougall and
Hungr (Hungr,
2010).
This run out analysis approach is based
on continuum-based dynamic simulation models which are based on equations of conservation
of mass and momentum.

 

3.     
Conclusion

Some details & parameters of recent landslide area will
be selected relative to that landslide before modelling the flow path. After
collecting that particular data and information, modelling will be commenced. Those
results should have to validate and check whether that landslide flow paths
modelling is correlated with original landslide. First of all we should focus
on processing of inventory map. So the particular landslide area will be
extracted from total area of study zone using the GIS software based on KLR
model. Then using the combination of TRIGRS and Scoops3D software, the
initiation zone of the landslide will be found out. Finally this result will be
applied in DAN 3D software to get a run out areas with the run out distance,
maximum depth of transport zone and velocity of flowing masses in transport
zone for the dynamic analysis of rapid flow slides and debris flows in the
landslides. This run out analysis approach is based on continuum-based dynamic
simulation models which are based on equations of conservation of mass and
momentum. Then the results will be validated and checked whether with original
landslide flow paths. If it will be validated with the original landslide flow
path then another landslide occurrence area due to the excessive rainfall will
be randomly selected and landslide flow path will be obtained with respect to
the different amount of the precipitation. From this critical value of
precipitation amount will be obtained and that will be checked with original
precipitation value. If this result will be validated with original
precipitation value then we can come to conclude that landslide will be
triggered due to excessive rainfall than critical value of precipitation. This
is act as an early warning system. Because, if we can predict the landslide
flow path during excessive rainfall than critical value of precipitation. Hence
we can avoid the landslide hazards, risks and vulnerability during the
landslides.

     Acknowledgements

I would like to convey my gratitude to all who
helped me to successfully conduct my research project.

First of all I would like to thank to Eng. Prof. S. A. S. Kulathilaka professor of civil
engineering department, University of Moratuwa for support to my research
project by giving proper guidance and data as a research supervisor. Next I
like to thank to Dr. Ashani Ranthunga to encourage and guide to write
the research proposal and research literacy view in proper way. Then I like to
convey my gratitude to my friends who are help to my project.

 

 

 

 

 

 

 

 

 

 

 

 

 

Bibliography

Olsen , M., & Mahalingam , R. (2016). Evaluation
of the in?uence of source and spatial resolution of DEMs on derivative
products used in landslide mapping. Geomat Nat Haz Risk, 7:1835–1855.
National Symposium On Landslides In Sri Lanka.
(1994). Srilanka: National Building Research Organization.
Bromhead , E. (2002). Three-dimensional stability analysis
of a coastal landslide at Hanover Point, Isle ofWight. Quart J Eng Geol
Hydrogeol , https://doi.org/10.1144/qjegh.35.1.79.
Chakraborty, A., & Goswami, D. (2016). Three
dimensional (3D)slope-stability analysis. International Journal of
Geotechnical Engineering, https://doi.org/10.1080/19386362.2016.1172807.
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Inc.
Mahalingam, R., & Olsen, M. (2016). Evaluation of the
influence of source and spatial resolution of DEMs on derivattive products
used in landslide mapping. Geomat Nat Haz Risk.
Viet, T. T. (2017). The TRIGRS model for rainfall-induced
landslides. Research gate.
Wei Chen. (2017). GIS-based landslide susceptibility
modelling: a comparative assessment of kernel logistic regression, Na€
?ve-Bayes tree, and alternating decision tree models. Geomatics, Natural
Hazards and Risk.
Wei Chen, Xiaoshen Xie, Jianbing Peng, Jiale Wang, Zhao
Duan, & Haoyuan. (2017). GIS-based landslide susceptibility modelling:. GEOMATICS,
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Wikipedia. (n.d.). Retrieved from
https://en.wikipedia.org/wiki/Causes_of_landslides:
https://en.wikipedia.org/wiki/Causes_of_landslides

 

 

 

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