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MODELING OF LANDSLIDE HAZARD ASSESSMENT
USING GEO-INFORMATICS TECHNIQUES
PhD SYNOPSIS
Submitted To
Gujarat Technological University
For The Degree
Of
Doctor of Philosophy
In
Civil Engineering
By
PHAM THAI BINH
Enrollment Number: 139997106007
(Batch 2013)
Supervisor Co-Supervisor
Dr. M.B Dholakia Dr. Indra Prakash
(LD En LD Engineering College, Ahmedabad BISAG, Gandhinagar
Ahmedabad, 2017
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1. Title of the Thesis and Abstract
1.1. Title of the Thesis
“Modeling of Landslide Hazard Assessment Using Geo-Informatics Techniques”
1.2. Abstract
Landslide is very common phenomenon all over the world causing loss of life,
property and infrastructure. One of the important ways to reduce these damages is to carry out
landslide hazard assessment to identify areas where and when landslide will occur. A number
of methods have been developed and applied for landslide hazard assessment but no
agreement has been reached for the best method applicable to all areas. With this objective,
present study has been carried out in part of Uttarakhand state, India and Mu Cang Chai
district, Viet Nam and a novel method Rotation Forest Fuzzy Rules Based Classifier Ensemble
(RFCE) model has been developed and applied in these two widely separated areas. This
model is based on a hybrid intelligent approach of two state of the art machine learning
methods of Rotation Forest (RF) ensemble and Fuzzy Unordered Rules Induction Algorithm
(FURIA) classifier. For doing this task, landslide inventory map has been prepared with the
help of remote sensing and field data. A total of fifteen landslide conditioning factors (slope
angle, slope aspect, elevation, curvature, plan curvature, profile curvature, soil, distance to
lineaments, lineament density, land cover, rainfall, distance to roads, road density, river
networks, distance to river, river density) have been selected for landslide hazard analysis at
the study areas using Geo-informatics techniques.
Other popular models such as Support Vector Machines (SVM), Logistic Regression
(LR), Fisher’s Linear Discriminant Analysis (FLDA), Naïve Bayes (NB), Bayesian Network
(BN), Multilayer Perceptron neural network (MLPN), Radial Basis Function neural network
(RBFNN), and Vote Feature Intervals (VFI) have also been applied in the study areas to
compare the results of predictive capability of these models with the proposed RFSCE model.
Performance of the models has been validated using Receiver Operating Characteristics
(ROC) curve and statistical analyzing methods. Analysis result shows that the novel landslide
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model RFCE has the highest predictive capability for landslide spatial prediction, followed by
SVM, LR, FLDA, BN, NB, MLP, RBF, and VFI, respectively in Uttarakhand Area, India and
also in Mu Cang Chai district, Viet Nam. Further, landslide hazard assessment of Mu Cang
Chai area (Viet Nam) has been done using RFSCE model in conjunction with probability
analysis of rainfall data to develop landslide hazard map.
2. Brief Description of the State of the Art of the Research Topic
Landslide is a well-known geo-hazard which causes the loss of human life and
property in many countries of the world. According to the Centre for Research on the
Epidemiology of Disasters, landslides are responsible for at least 17% casualties among the
deadly natural hazards throughout the world (Lacasse and Nadim, 2009). All over the world,
there were 2,620 deadly landslide events that have occurred within 6 years from 2004 to 2010
killing a total of 32,322 people (Petley, 2012). Approximately 18,000 people have been died
and about 5.5 million people have been affected due to landslides during the period of 1950 –
2009 in Asia, and the number of landslides in this region is relatively high in comparison to
other regions of the world (EM-DAT, 2010).
Landslide studies have been getting global attention not only because of increasing
awareness of socio-economic harmful impacts but also increasing pressure of urbanization on
the mountain regions (Aleotti and Chowdhury, 1999). Nowadays, because of increased
unplanned urbanization, increased regional precipitation due to climate changes, and
continued deforestation in landslide prone regions, landslide problems seem to be more
challenging in the future (Ercanoglu et al., 2004; Schuster, 1996).
Landslide hazard is defined as the probability of occurrences of potential landslide in
specified period of time in a given area (Varnes, 1984). Landslide hazard map, which is a
helpful tool for planning and decision making in landslide hazard management (Guzzetti et al.,
1999), shows which area is more prone to landslides in specified period of time. Based on
that, remedial measures can be taken in advance to mitigate the damages caused by landslides.
In recent years, many landslide hazard maps have been produced in many regions all over the
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world using Geographic Information System (GIS) technique (Guzzetti et al., 2005; Tien Bui
et al., 2013). GIS is an excellent and useful tool in order to produce landslide hazard map.
Nowadays, the data used in GIS is easier and quicker to receive due to the development of
Global Positioning Systems (GPS) and Remote Sensing (RS) techniques. Therefore, the
utilization of GIS becomes more and more popular in the spatial and temporal analysis of
landslides (Althuwaynee et al., 2012).
3. Definition of the Problem
Landside is a natural hazard which is described as a massive movement of materials
(soils, rocks, organics, etc.) from upslope to down slope under the effects of landform and
gravity (Highland, 2004; Van Dijke and van Westen, 1990; Varnes, 1978). In addition,
landslide can be understood as one of the natural movement processes of surface materials
which constitute the different shapes of earth surface (Varnes, 1984).
Landslide, in general, is a complex phenomenon because it is affected from an
interaction of numerous factors such as geology, geomorphology, hydrology, meteorology
(Varnes, 1984). Additionally, it can be influenced by natural processes and man-made
activities such as earthquakes, rainfall, volcanic activities, human activities such as slope
cutting and road construction on steep slopes, groundwater changes, or any combination of
these factors (Highland, 2004).
Landslide problem has got a lot of attention during recent decades. However, most of
landslide studies are related to spatial prediction of landslides. Moreover, only few attempts
have been made to establish temporal probability of landslides. This is mainly due to
limitation of availability of site specific data such as exact time, magnitude and velocity of
mass movements. In addition, none of methods has so far been identified during literature
survey as the best strategy to assess landslide hazard for all landslide prone areas. Therefore, a
number of landslide problems which are needed to be studied in detail include:
Evaluation and comparison of different available landslide models for choosing
the most suitable model for predicting spatial occurrence of landslides
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Development of new landslide models for better landslide spatial prediction
Development of landslide model for landslide temporal prediction
Development of landslide hazard assessment map.
4. Objective and Scope of Work
The overall objective of this research is to develop models for the assessment of
rainfall-induced landslide hazard using Remote Sensing and GIS. Main objectives of the
research are:
To study, validate and compare the available landslide susceptibility models;
To develop new model for spatial prediction of landslides;
To apply new susceptibility model for landslide hazard assessment;
To analyze and determine rainfall threshold for landslide initiation and
development of temporal probability model for landslide hazard assessment;
To carry out landslide hazard assessment.
5. Original Contribution by the Thesis
The work in the thesis is original with following contributions:
Landslide analysis based on various models will help to understand detail
mechanism of landslide occurrences and their characteristics;
Evaluation and comparison of different available models for landslide spatial
prediction will help in selection of suitable methods for development of
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landslide susceptibility maps which would be useful in landslide hazard
management;
The novel landslide model developed in this research will help to develop more
reliable landslide susceptibility maps not only in the study areas but also in
other landslide prone areas;
Landslide hazard assessment map developed would be useful for land use
planning and landslide hazard management.
The research work published in specialized reputed journals will help other
researchers in the utilization of existing and new techniques in the development
of further land slide models (see Section 9).
6. Methodology of Research, Results and Comparison
6. 1. Methodology
Methodology in the present study has been carried out in six main steps: (1) selection
of the study areas, (2) data collection and analysis, (3) comparison of available models for
landslide prediction, (4) development of model for spatial prediction of landslides, (5)
application of developed model for landslide hazard assessment, (6) temporal prediction for
landslide hazard assessment, and (7) landslide hazard assessment.
6. 1. 1. Selection of the Study Areas
Part of Uttarakhand district, India (Figure 1), and Mu Cang Chai district, Viet Nam
(Figure 2), which are affected by landslide hazard, have been selected as case studies for the
evaluation of existing machine learning models and development of new model for landslide
spatial prediction. Based on the study, a novel model developed has been applied at Mu Cang
Chai district, Vietnam for landslide hazard assessment.
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6. 1. 2. Data Collection and Analysis
Data has been collected from various government and non-government sources for
the preparation of landslide inventory and selection of landslide affecting factors. Analysis of
the remote sensing data in conjunction with historical and field data has been done using GIS
tool, considering the spatial and temporal relationship between landslide occurrences and
affecting factors for the landslide hazard assessment (Figure 1 and Figure 2).
Figure 1 Location of the Study Area of Uttarakhand area, India
Landslide affecting factors namely slope angle, slope aspect, elevation, curvature,
plan curvature, profile curvature, lithology, soil type, land cover, mean annual rainfall,
distance to lineaments, distance to roads, distance to rivers, lineament density, road density,
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and river density have been selected based on the analysis of the characteristics of the study
area and the nature of landslide occurrences.
Figure 2 Location of the Study Area of Mu Cang Chai District, Viet Nam
6. 1. 3. Application and Comparison of Available Models for Landslide Prediction
Different available machine learning methods have been applied for landslide spatial
prediction in the selected area of Uttarakhand state such as SVM, LR, FLDA, BN, NB, MLP,
RBF, and VFI. In addition, the predictive capability of different popular landslide models has
been validated using Area Under the ROC curve (AUC) and statistical indexes (SI) such as
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Accuracy (ACC), Kappa (K), Root Mean Square Error (RMSE) (Bennett et al., 2013; Bradley,
1997).
6. 1. 4. Development of Model for Spatial Prediction of Landslides
The novel classifier ensemble machine learning method namely Rotation Forest
Fuzzy Rules Based Classifier Ensemble (RFCE) has been developed and proposed for spatial
prediction of landslides Uttarakhand state, India. The RFCE is a hybrid method of Rotation
Forest ensemble and Fuzzy Unordered Rules Induction Algorithm classifier. Both are the
current state-of-the-art techniques which have been seldom used for spatial prediction of
landslides.
Rotation Forest ensemble (RF) was fist introduced by Rodriguez et al. (2006). This
is a relatively new ensemble technique to increase accuracy and diversity of base classifiers
(Rodriguez, 2007 ). RF uses principal component analysis to extract features to create sub-
training datasets for learning base classifiers (Koyuncu and Ceylan, 2013; Zhang and Zhang,
2009). Principal Component Analysis (PCA) is a base for multivarible data analysis, it
analyzes data table and matrix to extract the important information to make a set of new
orthogonal variables for further analyzing (Wold et al., 1987). RF has been applied in this
study for spatial prediction of landslides as an ensemble technique in classifier ensemble
framework.
Fuzzy Unordered Rules Induction Algorithm (FURIA) was first proposed by Hühn
and Hüllermeier (2009) is a modification and extension of a state-of-the-art rule learner of
well-known RIPPER algorithm presented in Cohen (1995). FURIA is based on fuzzy rules and
unordered rule sets instead of conventional (non-fuzzy) rules for classification (Hühn and
Hüllermeier, 2009). FURIA also uses rule stretching technique to deal with uncovered cases.
FURIA has significantly higher accuracy than RIPPER algorithm as well as C4.5 classifier
(Hühn and Hüllermeier, 2009). FURIA has been investigated and applied for spatial prediction
of lansdlides in this study. It has been utilized as a base classifier in classifier ensemble
framework.
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RFCE for landslide prediction has been carried out through two main steps: (i)
optimization, and (ii) classfication (Figure 3).
(i) Optimization: this step is carried out to optimize the input data for classification
step. At first, RF has been trained to divide raindomly original training dataset into sub-
training datasets. The iterations have been applied for dividing training datasets in different
ways. Spliting process is continous until the optimal sub-training datasets have been obtained.
In the present study, the training process of RF is the most effective with the number of
iterations of 17.
(ii) Classification: this step is to classify two classes of landslide and non-landslide
for spatial prediction of landslides using optimal sub-training datasets obtained from
optimization step in conjunction with FURIA algorithm. During the learning process of
FURIA, the minimum number of folds on the terminal node has been optimized to get the best
classification. In the present study, the minimum number of folds of 18 gives the best outcome
of classification step.
Figure 3. RFCE for Landslide Prediction (Note: F is FURIA)
Lastly, the RFCE model has been constructed by combination of these two steps for
landslide spatial prediction in the study area in which the number of iterations and the
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minimum number of observations at the terminal node have been selected as 17, and 18,
respectively to get the best performance of the RFCE model.
6. 1. 5. Application of the Developed Model for Landslide Hazard Assessment
In this step, the developed model RFCE has been applied in another area Mu Cang
Chai district to assess the applicability of developed model in other landslide prone areas. This
model (RFCE) has been used for landslide hazard assessment at Mu Cang Chi district, Viet
Nam.
6. 1. 6. Temporal Prediction for Landslide Hazard Assessment
The temporal prediction of landslides has been carried out in this step usingthe
probability analysis of rainfall data at the Mu Cang Chai district. Rainfall data of 5 years
(2008-2013) have been used for the analysis. Landslide locations with the occurrence date
records have been analyzed to calculate the temporal probability of landslides at the study area
for different return periods of years (1 year, 3 years, and 5 years).
6. 1. 7. Landslide Hazard Assessment
Landslide hazard assessment has been carried out in this step by combination of
landslide spatial and temporal prediction at Mu Cang Chai district.
6. 2. Results and Analysis
6. 2. 1. Model Comparison and Validation
Landslide models constructed from different methods have been validated as shown
in Table 1 and Figure 4. It can be observed that the developed novel landslide model has the
highest predictive capability (AUC = 0.955), followed by the SVM model (AUC = 0.950), the
MLP model (AUC = 0.938), the LR model (AUC = 0.922), the RBF model (AUC = 0.918),
the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), the NB model (AUC =
0.910), and the VFI model (AUC = 0.883), respectively.
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Table 1 Predictive capability of different landslide models
No SI RFCE SVM LR MLPN RBFN FLDA BN NB VFI
1 ACC 89.80 88.72 85.53 88.44 84.13 85.41 84.37 83.89 79.33
2 K 0.7961 0.775 0.711 0.7689 0.6825 0.708 0.687 0.678 0.5865
3 RMSE 0.2817 0.290 0.329 0.3039 0.3387 0.331 0.346 0.358 0.4918
4 AUC 0.955 0.950 0.922 0.938 0.918 0.921 0.915 0.910 0.883
Result of analysis shows that the developed novel landslide model which is widely
applicable is the most suitable for landslide spatial prediction in comparison to available
landslide models. In addition, all selected available models performed well for landslide
modeling in which the SVM model outperforms other models (MLP, LR, RBF, FLDA, NB,
NB, and VFI).
Figure 4 Analysis of the ROC Curve of Different Landslide Models
6. 2. 2. Landslide Hazard Assessment
Landslide hazard assessment has been done by combination of landslide spatial and
temporal prediction at Mu Cang Chai district in which landslide spatial prediction has been
carried out using the novel developed model RFCE whereas landslide temporal prediction has
been carried out using the probability analysis of rainfall data. Result of analysis shows that
the novel landslide model developed at part of Uttarakhand state (India) is also applicable for
0 10 20 30 40 50 60 70 80 90 100
100
90
80
70
60
50
40
30
20
10
0
100-Specificity
Se
nsitiv
ity
RECE (AUC = 0.955)
SVM (AUC = 0.950)
MLP (AUC = 0.938)
LR (AUC = 0.922)
RBF (AUC = 0.918)
0 10 20 30 40 50 60 70 80 90 100
100
90
80
70
60
50
40
30
20
10
0
100-Specificity
Se
nsitiv
ity
FLDA (AUC = 0.921)
BN (AUC = 0.915)
NB (AUC = 0.910)
VFI (AUC = 0.883)
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landslide spatial prediction at Mu Cang Chai district. The performance of the novel landslide
model is very good at Mu Cang Chai district, its predictive capability is even better than other
popular landslide models.
The temporal analysis results also show that the temporal probability of landslide
hazards for the Mu Cang district is 0.865, 0.998, and 1.00 for 1 return year, 3 return years, and
5 return years, respectively.
Finally, based on the landslide hazard assessment, landslide hazard map (Figure 5)
has been produced which will help in land use planning and landslide hazard management.
Figure 5 Landslide hazard map of the study area for 5 return years
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7. Achievements with Respect to Objectives
The most suitable method among available methods has been recognized for
spatial prediction of landslides.
The novel model RFCE has been developed for landslide spatial prediction
which can be applicable widely in other landslide prone areas.
Landslide susceptibility maps and landslide hazard maps constructed using
novel developed RFCE model would help in proper land use planning and
landslide hazard management of Uttarakhand, India and Mu Cang Chai district,
Vietnam.
8. Conclusions
Number of machine learning methods have been developed in the past and applied
for landslide hazard assessment but no agreement has been reached for the best method
applicable for all landslide prone areas. Therefore, a novel landslide model RFSCE has been
developed and applied in part of Himalayas (India) and Vietnam for the assessment and
prediction of landslides. This model has been compared with other well-known machine
learning models. Result of landslide spatial analysis shows that the novel developed landslide
model has the highest predictive capability, followed by SVM, LR, FLDA, BN, NB, MLP,
RBF, and VFI, respectively. Result of landslide temporal analysis shows that the temporal
probability of landslide occurrences is 0.865, 0.998, and 1.00 for 1 return year, 3 return years,
and 5 return years, respectively.
Overall, integration of a novel machine learning method integrated with probability
analysis of rainfall data indicates as promising approach for landslide hazard assessment
which could be applicable in other landslide prone areas. Based on the RFSCE model
landslide susceptibility and hazard maps have been developed for the study areas which can
help in landslide hazard management.
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9. Copies of Papers Published and a List of all Publications Arising from the Thesis
ISI (SCI, SCIE, SCOPUS) Journal Articles
1 Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide
susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study
of prediction capability of naïve bayes, multilayer perceptron neural networks, and
functional trees methods Theor Appl Climatol 122:1-19 doi:10.1007/s00704-015-1702-9
2 Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016) A comparative study
of different machine learning methods for landslide susceptibility assessment: A case
study of Uttarakhand area (India) Environmental Modelling & Software 84:240-250
doi:10.1016/j.envsoft.2016.07.005
3 Pham BT, Tien Bui D, Dholakia MB, Prakash I, Pham HV (2016) A comparative study
of least square support vector machines and multiclass alternating decision trees for
spatial prediction of rainfall-induced landslides in a tropical cyclones area Geotechnical
and Geological Engineering 34:1-18 doi:10.1007/s10706-016-9990-0
4 Pham BT, Tien Bui D, Pham HV, Le HQ, Prakash I, Dholakia MB (2016) Landslide
Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble
and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen
Bai Province (Viet Nam) Journal of the Indian Society of Remote Sensing:1-11
doi:10.1007/s12524-016-0620-3
5 Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Rotation forest fuzzy rule-based
classifier ensemble for spatial prediction of landslides using GIS Natural Hazards 83:1-
31 doi:10.1007/s11069-016-2304-2
6 Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Hybrid integration of Multilayer
Perceptron Neural Networks and machine learning ensembles for landslide susceptibility
assessment at Himalayan area (India) using GIS CATENA 149, Part 1:52-63
doi:10.1016/j.catena.2016.09.007
7 Tien Bui D, Ho T-C, Pradhan B, Pham B-T, Nhu V-H, Revhaug I (2016) GIS-based
modeling of rainfall-induced landslides using data mining-based functional trees
classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks
Environmental Earth Sciences 75:1-22 doi:10.1007/s12665-016-5919-4
8 Tien Bui D, Pham BT, Nguyen QP, Hoang N-D (2016) Spatial prediction of rainfall-
induced shallow landslides using hybrid integration approach of Least-Squares Support
Vector Machines and differential evolution optimization: a case study in Central Vietnam
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International Journal of Digital Earth 9:1-21 doi:10.1080/17538947.2016.1169561
9 Pham BT, Bui DT, Dholakia MB, Prakash I, Pham HV, Mehmood K, Le HQ (2016) A
novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility
assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS Geomatics,
Natural Hazards and Risk:1-23 doi:10.1080/19475705.2016.1255667
10 Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I
(2017). Shallow landslide susceptibility assessment using a novel hybrid intelligence
approach. Environmental Earth Sciences. 76. 60 doi:10.1007/s12665-016-6374-y
11 Pham BT, Tien Bui D, Indra P, Nguyen LH, Dholakia MB (2017) A Comparative Study
of Sequential Minimal Optimization Based Support Vector Machines, Vote Feature
Intervals and Logistic Regression in Landslide Susceptibility Assessment Using GIS
Environmental Earth Sciences (Accepted)
Journal Articles
1 Pham BT, Bui DT, Prakash I, Dholakia M (2016) Evaluation of predictive ability of
support vector machines and naive Bayes trees methods for spatial prediction of
landslides in Uttarakhand state (India) using GIS Journal of Geomatics 10:71 - 79
2 Pham BT, Nguyen MD, Le AH (2016) Shear resistance and stability study of
embankments using different shear resistance parameters of soft soils from laboratory and
field tests: a case study of Hai Phong city, Viet Nam International Journal for Scientific
Research and Development 3:330-334
3 Pham BT, Tien Bui D, Indra P, Dholakia M (2015) Landslide susceptibility assessment
at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of
frequency ratio method Int J Eng Res Technol 4:338-344
Conference Papers (peer-reviewed)
1 Pham BT, Tien Bui D, Indra P, Dholakia MB (2015) A comparison study of predictive
ability of support vector machines and naive bayes tree methods in landslide
susceptibility assessment at an area between Tehri Garhwal and Pauri Garhwal,
Uttarakhand state (India) using GIS Paper presented at the National Symposium on
Geomatics for Digital India & Annual Conventions of ISG & ISRS, Jaipur (India),
2 Pham BT, Tien Bui D, Pham HV Spatial Prediction of Rainfall Induced Landslides
Using Bayesian Network at Luc Yen District, Yen Bai Province (Viet Nam). In:
International Conference on Environmental Issues in Mining and Natural Resources
Development (EMNR 2016), Hanoi University of Mining and Geology (HUMG), Viet
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Nam, 2016. pp 1-10
3 Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Application of Classification and
Regression Trees for Spatial Prediction of Rainfall Induced Shallow Landslides in the
Uttarakhand Area (India) Using GIS. Paper presented at the 9th International
Geographical Union (IGU) Conference Land Use Change, Climate Extremes and
Disaster Risk Reduction University of Delhi, Delhi (India),
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