Landslide hazard map: tool for optimization of low-cost mitigation

Abstract

Background
Landslide hazard mapping is a fundamental tool for disaster management activities in fragile mountainous terrains. The main purpose of this study is to carry out landslide hazard assessment by weights-of-evidence modelling and prepare optimized mitigation map in the Higher Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the North-West marginal hills of the Achham. Thematic maps representing various factors that are related to landslide activity were generated using field data and GIS techniques. Landslide events of the old landslides were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. ResultsThe analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value. The hazard map prepared with five hazard classes viz. Very high, High, Moderate, Low and Very Low was used to determine the location of prime causative factors responsible for instability. Spatial distribution of causative factor was correlated with the mechanism and scale of failure. For the mitigation of such shallow-seated failure, bioengineering techniques (i.e. grass plantation, shrubs plantation, tree plantation along with small scale civil engineering structures) are taken as cost-effective and sustainable measures for the least developed country like Nepal. Based on prime causitive factors and required bioengineering techniques for stabilization of unstable road side slopes, mitigation map is prepared having 14 classes of mitigation measures. Conclusion
The mitigation map reveled only 6.8% road side slopes require retaining structures however that more than half of the instable slope can be treated with simple vegetative techniques. Therefore, high hazard doensnot demand expensive structures to mitigate it in each every case.

Full text

R E S E A R C H Open Access
Landslide hazard map: tool for optimization
of low-cost mitigation
Bhim Kumar Dahal
1*
and Ranjan Kumar Dahal
2
Abstract
Background: Landslide hazard mapping is a fundamental tool for disaster management activities in fragile
mountainous terrains. The main purpose of this study is to carry out landslide hazard assessment by weights-of-
evidence modelling and prepare optimized mitigation map in the Higher Himalaya of Nepal. The modelling was
performed within a geographical information system (GIS), to derive a landslide hazard map of the North-West
marginal hills of the Achham. Thematic maps representing various factors that are related to landslide activity
were generated using field data and GIS techniques. Landslide events of the old landslides were used to assess
the Bayesian probability of landslides in each cell unit with respect to the causative factors.
Results: The analysis suggests that geomorphological and human-related factors play significant roles in
determining the probability value. The hazard map prepared with five hazard classes viz. Very high, High, Moderate,
Low and Very Low was used to determine the location of prime causative factors responsible for instability. Spatial
distribution of causative factor was correlated with the mechanism and scale of failure. For the mitigation of such
shallow-seated failure, bioengineering techniques (i.e. grass plantation, shrubs plantation, tree plantation along with
small scale civil engineering structures) are taken as cost-effective and sustainable measures for the least developed
country like Nepal. Based on prime causitive factors and required bioengineering techniques for stabilization of
unstable road side slopes, mitigation map is prepared having 14 classes of mitigation measures.
Conclusion: The mitigation map reveled only 6.8% road side slopes require retaining structures however that more
than half of the instable slope can be treated with simple vegetative techniques. Therefore, high hazard doensnot
demand expensive structures to mitigate it in each every case.
Keywords: Bioengineering, GIS, Hazard, Landslide, Mitigation, Rainfall, Weight-of-evidence modeling
Background
In mountains of Himalayas, landslides are frequent
phenomenon as the mountain building process and in
interference with human activity they become a prob-
lem. Mountain slope failure is mainly provoked by
combine effect of intrinsic and extrinsic parameters.
The extrinsic events like rainfall and earthquake trig-
ger slope. Similarly, intrinsic parameters like bedrock
geology, geomorphology, soil depth, soil type, slope
gradient, slope aspect, slope curvature, land use, eleva-
tion, engineering properties of the slope material, land
use pattern, drainage pattern and so on have vital roles
in the landslide occurrence.
Varnes (1984) defined landslide hazard as the prob-
ability of occurrence of a landslide within a specified
period and within a given area. The landslide hazard
zonation is the process of classification of land with
equal landslide hazard value (Varnes 1984) and it pro-
vides information on the susceptibility of the terrain to
slope failures. This classified hazard map can be used
to prepare mitigation plan for the associated hazard.
Mitigation plan according to the hazard level is very
useful to optimize linear civil engineering structure like
road, which are long and passes through numerous
physical conditions (i.e. optimization in construction,
operation and maintenance). To reduce the Mitigation
technique for shallow seated instability, bioengineering
techniques are taken as sustainable and cost effective
measures (Deoja et al. 1991; Howell, 1999; Shrestha
2009; Rai 2010).
* Correspondence: dahal_bhim@hust.edu.cn
1
School of Civil Engineering and Mechanics, Huazhong University of Science
and Technology, 1037 Luoyu Road, Hongshan District, Wuhan, China
Full list of author information is available at the end of the article
Geoenvironmental Disasters
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Dahal and Dahal Geoenvironmental Disasters (2017) 4:8
DOI 10.1186/s40677-017-0071-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Study area
The study area is located in the northern hills of the
Achham, Nepal. The study area is within the Higher
Himalaya and belongs to the Kalikot and Slyanigad forma-
tion. Kalikot formation has Budhi Ganga gneiss group
consisting augen gneisses, granetic gneiss and feldspathic
schist and Ghattegad carbonates group consist bluish
crystalline limestone, calcareous schist and quartz biotite
schists. Similarly Salynigad formation consist aplite granite,
gneisses augen, gneisses and biotite gneisses. The study
area ranges from 980 to 2924 m from mean sea level.
The total watershed is taken as study area for purpose of
hazard mapping which is about of 65.46 km
2
whereas only
strip of 100 m either side of road is taken for preparation
of mitigation map. The mean annual precipitation ranges
from 1486 to 1739 mm. Most slopes face west, and the
slope gradient generally increases with increase in eleva-
tion. Colluvium is the main slope material above the
bedrock. The area is mainly covered with cultivated land.
In 2009, the study area experienced extreme events of
monsoon rainfall and faced 84 landslides. There were 91
old landslides traced from field survey and Arial Photo-
graph taken at different dates by Department of Survey.
Inventory for both old and new landslides are plotted in
GIS (Fig. 1). Because of a number of lakes in the study area,
currently different governmental and non-governmental
agencies have shown interest on the infrastructure devel-
opment of the area. Therefore, hazard analysis of the area
is necessary for the sustainability of such infrastructure.
Landslide hazard
Hazard is a source of risk that may cause damage to, or
loss of, life and property. Hazard can also be defined as
the probability of occurrence of a particularly damaging
phenomenon, within a specified period of time and
within a given area, because of a set of existing or pre-
dicted conditions in the given time and space. The
damaging phenomenon becomes a matter of concern
only when it entails a certain degree of damage or loss
to the population or the resources within its influence.
In the context of Nepal’s mountain the major hazard is
rainfall-induced landslide (Dahal et al. 2008).
To determine landslide hazard of any study area in-
trinsic (bedrock geology, geomorphology, soil depth, soil
type, slope gradient, slope aspect, slope convexity and
concavity, elevation, slope forming material, land use
pattern, drainage pattern, sediment transport and wetness
index) and extrinsic (rainfall, earthquakes, and volcanoes)
variables are used (Siddle et al. 1991; Wu and Sidle 1995;
Atkinson and Massari 1998; Dai et al. 2001; Çevik and
Topal 2003; Paudyal and Dhital 2005; Dahal et al.
2008). Since the extrinsic factor is difficult to estimate
instead of landslide hazard, the landslide susceptibility
mapping is done considering only intrinsic variables
(Dai et al. 2001). A landslide hazard zonation consists
of two different aspects (Van Westen et al. 2003): a)
The assessment of the susceptibility of the terrain for a
slope failure and b) The determination of the probabil-
ity that a triggering event occurs.
Fig. 1 Location of study area along with old and new landslides
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A region with terrain condition similar to the region
where landslide has occurred is considered to be suscep-
tibletolandslides(VanWestenandTerlien1996).Geo-
graphic Information Systems (GIS) with capability of
handling and integrating multiple intrinsic variables in rela-
tion to the spatial distribution of landslides has gained the
success in landslide hazard mapping (Dahal et al. 2008).
Methods
Hazard map
Weights-of-evidence modelling used to prepare landside
hazard map (Dahal et al. 2008) is based on Bayesian
probability model. This model was first developed and
used for mineral potential assessment (Bonham-Carter
2002). This method aided with GIS was very popular in
the field of mineral potential mapping (Emmanuel et al.
2000; Tangestani and Moore 2001). Zahiri et al. (2006)
used weights-of-evidence modelling for mapping of cliff
instabilities associated with mine subsidence. This
method has also been applied to landslide susceptibility
mapping (Lee et al. 2002; Van Westen et al. 2003, Lee
and Choi 2004, Lee et al. 2007; Neuhäuser and Terhorst
2007; Sharma and Kumar 2008). Dahal et al. 2008 used
this method for landslide hazard mapping. The method
calculates the weight for each landslide causative factor
based on the presence or absence of the landslides
within the area. The related mathematical relationships
are described below.
Wþ
i(Bonham-Carter 2002) and can be expressed as
below:
Wþ
i¼loge
PFjL
fg
PFjL
 ð1Þ
Similarly, negative weights of evidence, W−
i, as follows:
W−
i¼loge
P F jL

PFjL
 ð2Þ
Where, L is the presence of a landslide, F is presence
of a causative factor, F is the absence of causative factor
and L is absence of landslide.
A positive weight ( Wþ
i) indicates that the causative
factor is present at the landslide location, and the mag-
nitude of this weight is an indication of the positive
correlation between presence of the causative factor
and landslides. A negative weight ( W−
i) indicates an
absence of the causative factor and shows the level of
negative correlation.
Data preparation
The main step for landslide hazard mapping is data col-
lection and preparation of a spatial database from which
relevant factors can be extracted. The main feature of
this method is comparing the possibility of landslide
occurrence with observed landslides.
Based on field survey various causative factors were
identified, including slope, slope aspect, geology, flow
accumulation, relief, landuse, soil type, soil depth, distance
to road, curvature, wetness index, sediment transport
index and mean annual rainfall (Fig. 2). These thematic
map were prepared by using topographic maps and aerial
photographs taken by the Department of Survey, Govern-
ment of Nepal. Field surveys were carried out to prepare
landslide inventory, soil type, soil depth and landuse maps.
During survey landslides were plotted to the topographic
map of 1:50,000. Positions of landslide in map was deter-
mined by GPS. Meanwhile soil type and landuse were also
delineated in same topographic map. Whereas depth of
soil is estimated by the help of open-cut, terraces and
landslides. A landslide distribution map before and after
the extreme monsoon rainfall events in 2009 were pre-
pared after field survey (Fig. 2). These thematic data layer
were prepared using the GIS software ILWIS 3.3.
In this study the thirteen intrinsic variables and one
extrinsic variable was used for hazard analysis. All factor
maps with cell size of 10 m × 10 m were stored in raster
format. Each factor map was crossed with landslide in-
ventory map and weight map was prepared with the help
of series of commands written in script. Mathematical
expression used to calculate positive and negative weight
are as follows:
Wþ
i¼loge
N1
N1þN2
N3
N3þN4
() ð3Þ
W−
i¼Loge
N2
N1þN2
N4
N3þN4
() ð4Þ
Where N
1
,N
2
,N
3
and N
4
are No of cell units repre-
senting the presence of landslides and potential landslide
predictive factor, presence of landslides and absent of
potential landslide predictive factor, absence of landsides
and presence of potential landslide predictive factor and
absence of both landslides and potential landslide pre-
dictive factor respectively.
Landslide Hazard Index (LHI) map was prepared by
numerically adding the resultant weighted factor map
obtained by assigning weights of the classes of each
thematic layer:
LHI ¼WfSlope þWfAspect þWfDisdrn þWfCurv
þWfDisrd þWfFA þWfGeo þWfSoilt
þWfLandu þWfRelief þWfSoild þWfSTI
þWfWetI þWfRain:
ð5Þ
Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 3 of 9
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Three attribute maps of new, old and all landslides
were prepared from LHI values (Fig. 3), which were in
the range from −23.1 to 12.77. The ability of LHI to
predict landslide occurrences was verified using the
success rate curve (Chung and Fabbri 2003), prediction
rate, and effect analysis (Van Westen et al. 2003; Lee
and Choi 2004; Dahal et al. 2006). The success rate in-
dicates what percentage of all landslides occurs in the
classes with the highest value of susceptibility. When
old landslides are used for LHI calculation and new
landslides are used for prediction, the calculated accur-
acy rate is called prediction rate (Van Westen et al. 2003;
Lee et al. 2007) and is the most suitable parameter for
independent validation of LHI.
The success rate curves of all three maps are shown in
Fig. 4. These curves are the measures of goodness of fit.
In the case of new landslides, the success rate reveals
that 10% of the study area where LHI had a higher rank
could explain 68.66% of total new landslides. Likewise,
30% of higher LHI value could explain 95.07% of all
landslides. Similarly, for the cases of old landslides and
all landslides, 30% high LHI value could explain about
87.56 and 92.61% of total landslides respectively. Fig. 4
provides percentage coverage of landslides in various
higher rank percentage of LHI.
The prediction rate when LHI map of old landslides
crossed with new landslides is similar to the success
rates as above. It is independent, and when all maps
were combined for the LHI calculation, it gave 78.24%
prediction accuracy for the new landslides (Fig. 5).
More than 72% of the new landslides were well covered
by 30% of the high value of LHI calculated from the
old landslides.
For providing classified hazard maps, reference to
prediction rate curves (see Fig. 5) was made and five
landslide hazard classes were defined: very low (<25%
class of low to high LHI value), low (25–60% class of
low to high LHI value), moderate (60–75% class of low
to high LHI value), high (75–90% class of low to high
LHI value), and very high (>90% class of low to high
LHI value, i.e., most higher LHI values) were estab-
lished. Hazard map of overall watershed was prepared
first and area within road corridor was clipped for miti-
gation optimization (Fig. 6).
Fig. 2 Thematic maps
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Results and discussion
From the classified hazard map of the road corridor
(Fig. 6), each pixel of high hazard and very high hazard
class has been crossed with all intrinsic factors weight
map and top three were sorted out. From the study, it
was found that among 13 factor maps, landuse has the
highest contribution to the LHI value and then distance
to drain, soil depth, soil type, aspect and slope in de-
scending order (Table 1).
Jovani (2015) carried out study on national scale land-
slide hazard assessment along the road corridors of two
Caribbean islands, the study only gave the cost of landslide
clearance and repair of damage rather than mitigation. It
is clear that damaged caused by rainfall induced disaster
in 2010 to the highways is 5% of GDP of the Saint Lucia.
Fig. 4 Success rate curves of landslide hazard values calculated from
three types of landslide inventory maps
Fig. 5 Prediction rate curves of landslide hazard values calculated
from the inventory map of the old landslides
Fig. 3 Landslide Hazard Index map
Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 5 of 9
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Anbalagan et al. (2008) prepared a meso-scale land-
slide hazard zonation mapping and suggested that
planner should avoid the high hazard area or take
precautionary measures during implementation. These
researches are basically either for planning or for re-
pair and maintenance. Still there is very few literature
about the use of hazard map for mitigation aspect.
Siddan and Veerappan (2014) prepared hazard zon-
ation map for a highway section. They have proposed
some general mitigation measures like concrete
ditches, slope flattening, benching, anchoring etc.
which are either expensive or not suitable for the area
having high relative relief like Himalayas.
This paper is focused on low cost mitigation measures
for rural infrastructures. Bioengineering techniques, use of
living plants in conjunction with small scale civil engineer-
ing structures, are taken as the low cost mitigation tech-
niques. These techniques are taken as cost-effective and
sustainable measures for the least developed country like
Nepal and are very useful for mitigation of shallow-seated
Fig. 6 Landslide hazard zonation map: aOverall watershed and bTimilsen-Ramaroshan Road corridor
Table 1 Effect analysis of the factor map
Factor map/Class % presence in top three w
+
Land use; Barren land 24
Distance to drain; 20–50 m, 50–100 m and >200 m 19
Soil depth; Shallow 19
Soil type; Colluvium 17
Aspect; S-W 9
Slope; Steep 8
Other 4
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failure. Rai (2010), conducted comparative study and
concluded that cost of conventional civil engineering
techniques is double to the cost of bioengineering tech-
niques for stabilizing same landslide site (Table 2).
Besides low construction and maintenance cost it has
many socio-economic and environmental benefits.
Bioengineering techniques (i.e. grass plantation, shrubs
plantation, tree plantation along with small scale civil
engineering structures) for mitigation of shallow seated
instability problem depends on the characteristics of fail-
ure (Howell 1999; Deoja et al. 1991). Mechanism of fail-
ure is depend on presence of different intrinsic factor
and its classes. Considering the fact that every class has
some distinct characteristics and mechanism of failure,
therefore mitigation measure is proposed to overcome
the effect of each class on slope stability (Table 3).
Mitigation map
Classified hazard map was statistically analysed to find
out the most predominating factors causing landslide.
The analysis of each cell unit of hazard map shows that
there are altogether twelve classes or combination of dif-
ferent classes responsible for instability. These classes in-
clude eight predominating classes of different factor
maps whereas, three are combination of two classes and
a combination of three classes.
The mitigation measures proposed based on different
predominating class is overlapped in each and every cell
units. As the result, the concise mitigation representa-
tion of study area is presented in matrix form (Table 4).
Mitigation map of the study area was prepared after
conducting analysis in ILWIS and EXCEL. Low cost miti-
gation raster map of Timilsen-Ramaroshan District Road
was prepared (Fig. 7) by clipping mitigation map of study
area and road corridor map. Mitigation map depicts that
overall mitigation structure can be classified in fourteen
classes derived from seven basic structure types.
Table 3 Mitigation measures per class and combination
Class Problems Mitigation Code
S-W aspect Erosion Vegetation A
Barren land Erosion Vegetation A
Loose colluvium Erosion Retaining wall, vegetation B
Shallow soil depth Slips Vegetation A
Steep slope Slips Retaining wall, benching G
Distance to Drain 20–50 m Scour, Drainage Toe protection, surface and sub-surface drain C
Distance to Drain 50–100 m Drainage Surface and sub-surface drain D
Distance to Drain >200 m Drainage Surface drain E
Combination of 3,4 Erosion Retaining wall, vegetation B
Combination of 3,5 Erosion, flow Retaining wall, benching and vegetation F
Combination of 4,5 Slips Retaining wall, benching G
Combination of 3,4,5 Erosion, flow Retaining wall, benching and vegetation F
Table 2 Cost comparison of conventional and bioengineering
mitigation works (Rai 2010)
Item Unit Quantity Cost (NRs.)
Cost of Bioengineering works 5,875,704.00
Slide clearance m
3
390 26,910.00
Construction of plum concrete wall m
3
1350 4,872,150.00
Construction of gabion wall m
3
120 157,920.00
Construction of dry wall m
3
107 95,444.00
Rill and ridge formation m
2
85 15,045.00
Slope trimming m
2
1807 38,140.00
Backfilling m
3
896 103,040.00
Installation of sub-soil drain m 180 177,480.00
Coir netting m
2
877 156,106.00
Grass plantation m
2
1893 132,510.00
Brushlayering m 581 18,011.00
Grass seeds broadcasting on slope m
2
3380 64,220.00
Shrub seeds sowing on slope m
2
948 10,428.00
Fruit plantation no 150 600.00
Bamboo plantation no 50 7700.00
Cost of Civil Engineering Works 12,201,833.00
Earth work in excavation m
3
2581 296,815.00
Earth work in backfilling m
3
7350 845,250.00
Plum Concrete revetment wall (1:2:4) m
3
1350 4,832,100.00
Gabion wall m
3
2123 2,793,868.00
PCC (1:2:4) m
3
280 1,079,400.00
Cement masonry cut drain in (1:4) Rm. 200 727,000.00
Cement masonry surface drain (1:4) Rm. 120 469,800.00
Cement masonry chute (1:4) Rm. 100 643,100.00
Grass Plantation m
3
7350 514,500.00
(1USD =NRS 98.17 on 09 Oct 2010, Source: Nepal Rastra Bank)
Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 7 of 9
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The mitigation map of the road corridor clearly depict
that 60% of road side slope is naturally stable and
doesn’t required mitigation works. The remaining 40%
slope is required different mitigation measures (Fig. 8).
The area required vegetation for stabilization is found to
be 20.4%, similarly 6.8% of the road side slope required
retaining wall and 16% of road side slope required drai-
ninge facility. Since the terrain is steep with high relative
relief and the slope will be steeper after construction,
slope flattening and benching necessary for 16% of un-
stable roadside slope.
Conclusions
Landslide hazard mapping is essential in delineating
landslide prone areas and optimizing low cost mitiga-
tion measures in mountainous regions. Amongst vari-
ous techniques, this study applied weights-of-evidence
modelling for landslide hazard analysis, to the northern
mountain in the Higher Himalaya of Achham, Nepal.
There are very few literatures available for mitigation
mapping by using hazard zonation. Some authors has
general recommendation of mitigation measures for the
high hazard zone but not site specific. In this context,
this study will fill the existing gap for the use of hazard
zonation for site specific mitigation mapping. From the
prepared mitigation map, the conclusions are drawn as
follows:
The first and the most important conclusion of this
research is, mitigation measure for slope stability is
more realistic and sustainable only after considering
landslide hazard index as well as the causative
factors. The mitigation map of the study area
revealed that only 6.8% road side slopes required
retaining structures. Therefore, high hazard always
doesn’t demand expensive structures to stabilize it.
More often, they are stabilized by very simple
measure as per its mechanism and causing factor
of instability.
More than half of (20.4% out of 40% area) the
instable area can be stabilized with simple
bioengineering techniques like grass and shrubs
plantation and remaining half will be stabilized in
conjunction with small scale civil engineering
structures. Therefore, the mitigation approach is
much more cost effective in terms of construction
cost (Rai 2010) in addition to the social and
environmental benefits. These techniques are
functionally sound on stabilizing the shallow seated
landslides which is the major problem in Himalayan
region during construction and operation of roads.
The concept of mitigation matrix prepared in this
research is new concept and is very useful to deal
with classified mitigation hazard map for Nepalese
mountain slopes.
The optimized mitigation measures might reduce
the blockade time of road and improve life standard
of the people living in remote villages of Achham
and Kalikot districts.
Table 4 Mitigation matrix
Class S-W Barren Distance to Drain
20–50 50–100 >200 m
None A A C D E
Loose colluvium B B BC BD BE
Shallow soil depth A A AC AD AE
Steep slope GA GA GC GD GE
Combination of 3, 4 B B BC BD BE
Combination of 3, 5 F F FC FD FE
Combination of 4, 5 GA GA GC GD GE
Combination of 3, 4, 5 F F FC FD FE
Fig. 7 Mitigation measures for Timilsen-Ramaroshan Road
Fig. 8 Distribution of mitigation measures by type required
for stabilization
Dahal and Dahal Geoenvironmental Disasters (2017) 4:8 Page 8 of 9
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Abbreviations
GDP: Gross Domestic Product; GIS: Geological Information System;
LHI: Landslide Hazard Index
Acknowledgement
Authors would like to acknowledge local people of the study area for
providing assistance, help and co-operation during field data collection.
We would like to thanks Mr. Chitra Thapa and Mr. Diwakar K C for their
valuable help and advice during this research.
Authors’contributions
BKD carried out data collection, conducted analysis and drafted manuscript.
RKD has prepared research design, monitored outcome and reviewed
manuscript. Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Author details
1
School of Civil Engineering and Mechanics, Huazhong University of Science
and Technology, 1037 Luoyu Road, Hongshan District, Wuhan, China.
2
Geodisaster Research Center, Central Department of Geology, Tribhuvan
University, Kritipur, Kathmandu, Nepal.
Received: 17 September 2016 Accepted: 28 January 2017
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