1

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

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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),

References

Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment: summary review and new

perspectives. Bulletin of Engineering Geology and the environment, 58(1), 21-44.

Althuwaynee, O.F., Pradhan, B., Lee, S., 2012. Application of an evidential belief function

model in landslide susceptibility mapping. Computers & Geosciences, 44, 120-135.

Bennett, N.D., Croke, B.F., Guariso, G., Guillaume, J.H., Hamilton, S.H., Jakeman, A.J.,

Marsili-Libelli, S., Newham, L.T., Norton, J.P., Perrin, C., 2013. Characterising

performance of environmental models. Environmental Modelling & Software, 40, 1-

20.

Bradley, A.P., 1997. The use of the area under the ROC curve in the evaluation of machine

learning algorithms. Pattern recognition, 30(7), 1145-1159.

Cohen, W.W., 1995. Fast effective rule induction, Proceedings of the twelfth international

conference on machine learning, pp. 115-123.

EM-DAT, 2010. International disasters database. http://www.emdat.be.

Ercanoglu, M., Gokceoglu, C., Van Asch, T.W., 2004. Landslide susceptibility zoning north

of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards, 32(1),

1-23.

Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., 1999. Landslide hazard evaluation: a

review of current techniques and their application in a multi-scale study, Central Italy.

Geomorphology, 31(1), 181-216.

Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F., 2005. Probabilistic

landslide hazard assessment at the basin scale. Geomorphology, 72(1–4), 272-299.

Highland, L., 2004. Landslide types and processes. 2327-6932.

18

Hühn, J., Hüllermeier, E., 2009. FURIA: an algorithm for unordered fuzzy rule induction.

Data Mining and Knowledge Discovery, 19(3), 293-319.

Koyuncu, H., Ceylan, R., 2013. Artificial neural network based on rotation forest for

biomedical pattern classification, Telecommunications and Signal Processing (TSP),

2013 36th International Conference on. IEEE, pp. 581-585.

Lacasse, S., Nadim, F., 2009. Landslide Risk Assessment and Mitigation Strategy. En Sassa,

k. and Canuti, P.(eds.) Landslides–Disaster Risk Reduction: 31-61 p. Springer-Verlag,

Berlin.

Petley, D., 2012. Global patterns of loss of life from landslides. Geology, 40(10), 927-930.

Rodriguez, J.J., 2007 Rotation Forest and Random Oracles: Two Classifier Ensemble Methods

Computer-Based Medical Systems. IEEE Maribor pp. 3

Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J., 2006. Rotation Forest: A New Classifier

Ensemble Method. Pattern Analysis and Machine Intelligence, IEEE Transactions on,

28(10), 1619-1630.

Schuster, R.L., 1996. Socioeconomic significance of landslides. Landslides: Investigation and

Mitigation. Washington (DC): National Academy Press. Transportation Research

Board Special Report, 247, 12-35.

Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, Ø.B., 2013. Regional prediction of

landslide hazard using probability analysis of intense rainfall in the Hoa Binh province,

Vietnam. Natural hazards, 66(2), 707-730.

Van Dijke, J.J., van Westen, C.J., 1990. Rockfall hazard, a geomorphological application of

neighbourhood analysis with ILWIS. ITC Journal, 1, 40-44.

Varnes, D.J., 1978. Slope movement types and processes. Transportation Research Board

Special Report(176).

Varnes, D.J., 1984. Landslide hazard zonation: a review of principles and practice, UNESCO,

Paris.

Wold, S., Esbensen, K., Geladi, P., 1987. Principal component analysis. Chemometrics and

intelligent laboratory systems, 2(1-3), 37-52.

Zhang, C.-X., Zhang, J.-S., 2009. A Novel Method for Constructing Ensemble Classifiers.

Statistics and Computing, 19(3), 317-327.