Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area

doi:10.1080/19475705.2010.498151

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Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area

Journal Article•DOI•

Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area

Biswajeet Pradhan¹, Hyun-Joo Oh, Manfred F. Buchroithner¹•Institutions (1)

Dresden University of Technology¹

24 Aug 2010-Geomatics, Natural Hazards and Risk (Taylor & Francis)-Vol. 1, Iss: 3, pp 199-223

TL;DR: In this article, the authors demonstrate the application of the weights-of-evidence model (a Bayesian probability model) to landslide susceptibility mapping using geographical remote sensing (GIS) in a tropical hilly area of Malaysia.

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Abstract: A study demonstrating the application of the weights-of-evidence model (a Bayesian probability model) to landslide susceptibility mapping using geographical remote sensing (GIS) in a tropical hilly area of Malaysia is presented. In the first stage, a landslide related spatial database was created. Seven landslide conditioning factors were considered for the susceptibility analysis. Using landslide location and a spatial database containing information such as topography, soil, lithology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factor's rating for the Cameron Highlands area in Malaysia. The topographic database including information on slope angle, slope aspect, plan curvature and distance from drainage was developed from a digital elevation model (DEM); the lithology and the distance from the lineament were derived from the geological database; soil texture was derived from the soil database; land cover and normalized difference vegetation index (NDVI...

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Journal Article•DOI•

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

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Biswajeet Pradhan¹•Institutions (1)

Universiti Putra Malaysia¹

01 Feb 2013-Computers & Geosciences

TL;DR: In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.

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870 citations

Journal Article•DOI•

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

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Hamid Reza Pourghasemi¹, Biswajeet Pradhan², Candan Gokceoglu³•Institutions (3)

Tarbiat Modares University¹, Universiti Putra Malaysia², Hacettepe University³

26 May 2012-Natural Hazards

TL;DR: In this paper, the authors used fuzzy logic and analytical hierarchy process (AHP) models to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran.

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Abstract: The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (55 landslides) for training the models and the remaining 30 % (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7 %) performed better than AHP (81.1 %) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.

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732 citations

Cites background or methods from "Weights-of-evidence model applied t..."

...Statistical models such as logistic regression also have been used in landslide susceptibility mapping (Wang and Sassa 2005; Lee and Sambath 2006; Lee and Pradhan 2007; Pradhan et al. 2008, 2010b, c; Tunusluoglu et al. 2008; Nefeslioğlu et al. 2008a; Pradhan 2010c)....
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...More recently, new techniques have been used for landslide susceptibility mapping such as neuro-fuzzy (Kanungo et al. 2005; Lee et al. 2009; Pradhan et al. 2010d; Vahidnia et al. 2010; Sezer et al. 2011; Oh and Pradhan 2011), support vector machine (SVM) (Brenning 2005; Yao et al. 2008; Yilmaz…...
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...…al. 2012); fuzzy logic (Ercanoglu and Gokceoglu 2002, 2004; Lee 2007; Pradhan and Lee 2009; Pradhan 2011a, b; Akgun et al. 2012) and artificial neural network models (Lee et al. 2004b; Pradhan and Lee 2007, 2009, 2010b; Biswajeet and Saied 2010; Pradhan et al. 2010a; Pradhan and Buchroithner 2010)....
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...This method is already widely used as a measure of performance of a predictive rule (Yesilnacar and Topal 2005; Van Den Eeckhaut et al. 2006; Pradhan et al. 2010a; Pourghasemi et al. 2012a, b)....
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Journal Article•DOI•

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

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Mahyat Shafapour Tehrany¹, Biswajeet Pradhan¹, Mustafa Neamah Jebur¹•Institutions (1)

Universiti Putra Malaysia¹

06 May 2014-Journal of Hydrology

TL;DR: In this article, the authors proposed an ensemble weight-of-evidence (WoE) and support vector machine (SVM) model to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis.

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608 citations

Journal Article•DOI•

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya

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Krishna Chandra Devkota¹, Amar Deep Regmi², Hamid Reza Pourghasemi³, Kohki Yoshida², Biswajeet Pradhan⁴, In Chang Ryu¹, Megh Raj Dhital⁵, Omar F. Althuwaynee⁴ - Show less +4 more•Institutions (5)

Kyungpook National University¹, Shinshu University², Tarbiat Modares University³, Universiti Putra Malaysia⁴, Kathmandu⁵

01 Jan 2013-Natural Hazards

TL;DR: In this article, a landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models.

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Abstract: Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling–Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling–Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

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542 citations

Cites background or methods from "Weights-of-evidence model applied t..."

...…al. 2011; Vahidnia et al. 2010; Oh and Pradhan 2011), artificial neural networks (Bui et al. 2012a; Lee et al. 2007; Pradhan and Lee 2009, 2010a, b; Pradhan et al. 2010a, b, d; Pradhan and Buchroithner 2010; Pradhan and Pirasteh 2010; Pradhan 2011a; Poudyal et al. 2010; Yilmaz 2009a, b, 2010a, b;…...
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...…methods have been proposed by several investigators, including weights-of-evidence methods (Bonham-Carter 1991; Neuhäuser and Terhorst 2007; Pradhan et al. 2010d; Regmi et al. 2010a; Pourghasemi et al. 2012a, b), modified Bayesian estimation (Chung and Fabbri 1999), weighting factors,…...
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...Also, a given road segment may act as a barrier, a net source, a net sink or a corridor for water flow, and depending on its location in the area, it usually serves as a source of landslides (Pradhan et al. 2010a)....
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...…factors (Saha et al. 2005), probabilisticbased frequency ratio model (Chung and Fabbri 2003, 2005; Lee and Pradhan 2006, 2007; Akgün et al. 2008; Pradhan et al. 2010c 2011, 2012), certainty factors (Pourghasemi et al. 2012a), information values (Saha et al. 2005), modified Bayesian estimation…...
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Journal Article•DOI•

Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran

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Hamid Reza Pourghasemi¹, Majid Mohammady¹, Biswajeet Pradhan²•Institutions (2)

Tarbiat Modares University¹, Universiti Putra Malaysia²

01 Oct 2012-Catena

TL;DR: In this article, the authors used an index of entropy and conditional probability model to produce landslide susceptibility maps at Safarood basin, Iran using two statistical models such as an Index of Entropy and Conditional Probability and to compare the obtained results.

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Abstract: Landslide susceptibility mapping is essential for land use planning and decision-making especially in the mountainous areas. The main objective of this study is to produce landslide susceptibility maps at Safarood basin, Iran using two statistical models such as an index of entropy and conditional probability and to compare the obtained results. At the first stage, landslide locations were identified in the study area by interpretation of aerial photographs and from field investigations. Of the 153 landslides identified, 105 (≈70%) locations were used for the landslide susceptibility maps, while the remaining 48 (≈30%) cases were used for the model validation. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, distance to faults, distance to rivers, distance to roads, topographic wetness index (TWI), stream power index (SPI), slope–length (LS), land use, and plan curvature were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by index of entropy and conditional probability models. Finally, the ROC (receiver operating characteristic) curves for landslide susceptibility maps were drawn and the areas under the curve (AUC) were calculated. The verification results showed that the index of entropy model (AUC=86.08%) performed slightly better than conditional probability (AUC=82.75%) model. The produced susceptibility maps can be useful for general land use planning in the Safarood basin, Iran.

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386 citations

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References

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Open Access

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Journal Article•DOI•

Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy

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Fausto Guzzetti, Alberto Carrara, Mauro Cardinali, Paola Reichenbach

01 Dec 1999-Geomorphology

TL;DR: In this paper, the authors used geomorphological information to assess areas at high landslide hazard, and help mitigate the associated risk, and found that despite the operational and conceptual limitations, landslide hazard assessment may indeed constitute a suitable, cost-effective aid to land-use planning.

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2,146 citations

"Weights-of-evidence model applied t..." refers background in this paper

...( 1999 ) summarized various landslide hazard mapping approaches....
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...Guzzetti et al. (1999) summarized various landslide hazard mapping approaches....
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Abstract: Chapter headings. Introduction to GIS. Spatial data models. Spatial data structures. Spatial data input. Visualization and query of spatial data. Spatial data transformations. Tools for map analysis: single maps. Tools for map analysis: map pairs. Tools for map analysis: multiple maps.

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1,640 citations

Journal Article•DOI•

Landslide hazard and risk zonation—why is it still so difficult?

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C.J. van Westen¹, T. W. J. van Asch², R. Soeters¹•Institutions (2)

International Institute of Minnesota¹, Utrecht University²

01 May 2006-Bulletin of Engineering Geology and the Environment

TL;DR: In this paper, the authors review the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability.

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Abstract: The quantification of risk has gained importance in many disciplines, including landslide studies. The literature on landslide risk assessment illustrates the developments which have taken place in the last decade and that quantitative risk assessment is feasible for geotechnical engineering on a site investigation scale and the evaluation of linear features (e.g., pipelines, roads). However, the generation of quantitative risk zonation maps for regulatory and development planning by local authorities still seems a step too far, especially at medium scales (1:10,000–1:50,000). This paper reviews the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability. An overview of recent developments in the different approaches to landslide hazard and risk zonation at medium scales is given. The paper concludes with a number of new advances and challenges for the future, such as the use of very detailed topographic data, the generation of event-based landslide inventory maps, the use of these maps in spatial-temporal probabilistic modelling and the use of land use and climatic change scenarios in deterministic modelling.

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1,034 citations

"Weights-of-evidence model applied t..." refers background in this paper

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Journal Article•DOI•

Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong

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Fuchu Dai¹, C.F. Lee²•Institutions (2)

Chinese Academy of Sciences¹, University of Hong Kong²

15 Jan 2002-Geomorphology

TL;DR: In this article, the authors used a Geographical Information Systems (GIS) database, compiled primarily from existing digital maps and aerial photographs, to describe the physical characteristics of landslides and the statistical relations of landslide frequency with the physical parameters contributing to the initiation of landslide on Lantau Island in Hong Kong.

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989 citations

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Journal Article•DOI•

Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

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Cees J. van Westen¹, Enrique Castellanos¹, Sekhar L. Kuriakose¹•Institutions (1)

International Institute of Minnesota¹

01 Dec 2008-Engineering Geology

TL;DR: In this article, a review of the trends in collecting spatial information on environmental factors with a focus on Digital Elevation Models, geology and soils, geomorphology, land use and elements at risk is given.

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986 citations

"Weights-of-evidence model applied t..." refers background in this paper

...Landslide hazard and risk analysis, like many other forms of risk management of either natural or anthropogenic related hazards (Van Westen et al. 2006, 2008, Zezere et al. 2006), is a relatively new discipline....
[...]