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Journal ArticleDOI

The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan

01 Feb 2005-Geomorphology (Elsevier)-Vol. 65, Iss: 1, pp 15-31
TL;DR: In this paper, a landslide susceptibility map in the Kakuda-Yahiko Mountains of Central Japan is presented, where the authors use logistic regression to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters such as slope angle and lithology.
About: This article is published in Geomorphology.The article was published on 2005-02-01. It has received 1449 citations till now. The article focuses on the topics: Landslide & Slope stability.
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors presented a study of the relationship between geotechnical engineering and geosciences and geophysics at the University of New South Wales and U.S. Geological Survey.

1,186 citations

Journal ArticleDOI
TL;DR: In this paper, a critical review of statistical methods for landslide susceptibility modelling and associated terrain zonations is presented, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria.

957 citations


Cites background from "The application of GIS-based logist..."

  • ...Only after the year 2000, success/prediction rate (Chung and Fabbri, 1999, 2006) curves, and Receiver Operating Characteristic (ROC) curves, Ayalew and Yamagishi, 2005) have become popular, together with related indexes (Van Den Eeckhaut et al., 2006; Frattini et al., 2010; Rossi et al., 2010)....

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Journal ArticleDOI
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.

870 citations

Journal ArticleDOI
TL;DR: This study introduces a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods and demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptible mapping.
Abstract: Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

861 citations

Journal ArticleDOI
TL;DR: In this article, a probabilistic model is proposed to determine landslide hazard at the basin scale, where landslides will occur, how frequently they will occur and how large they will be.

818 citations

References
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Book
09 Oct 2001
TL;DR: The second edition of the Second Edition of the Logistic regression model as discussed by the authors is the most complete version of the first edition and includes a discussion of the relationship between linear regression and logistic regression.
Abstract: Series Editor's Introduction Author's Introduction to the Second Edition 1. Linear Regression and Logistic Regression Model 2. Summary Statistics for Evaluating the Logistic Regression Model 3. Interpreting the Logistic Regression Coefficients 4. An Introduction to Logistic Regression Diagnosis Ch 5. Polytomous Logistic Regression and Alternatives to Logistic Regression 6. Notes Appendix A References Tables Figures

4,046 citations


"The application of GIS-based logist..." refers background or methods in this paper

  • ...The pseudo R value, which can be calculated from 1 (ln L/ln L0), cautiously indicates how the logit model fits the dataset (Menard, 1995)....

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  • ...A mirror image will be obtained for a negative coefficient (Menard, 1995)....

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  • ...The principle of logistic regression (LR) rests on the analysis of a problem, in which a result measured with dichotomous variables such as 0 and 1 or true and false, is determined from one or more independent factors (Menard, 1995)....

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Journal ArticleDOI
TL;DR: In this article, the authors describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications.
Abstract: The topography of a catchment has a major impact on the hydrological, geomorphological. and biological processes active in the landscape. The spatial distribution of topographic attributes can often be used as an indirect measure of the spatial variability of these processes and allows them to be mapped using relatively simple techniques. Many geographic information systems are being developed that store topographic information as the primary data for analysing water resource and biological problems. Furthermore, topography can be used to develop more physically realistic structures for hydrologic and water quality models that directly account for the impact of topography on the hydrology. Digital elevation models are the primary data used in the analysis of catchment topography. We describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications. Some hydrologic models that make use of digital representations of topography are also considered.

2,855 citations

Journal ArticleDOI

2,365 citations

Journal ArticleDOI
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.

2,146 citations


"The application of GIS-based logist..." refers methods in this paper

  • ...Landslide susceptibility mapping using either multivariate or bivariate statistical approaches analyzes the historical link between landslide-controlling factors and the distribution of landslides (Guzzetti et al., 1999)....

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  • ...Hence, qualitative or semiquantitative methods are often useful for regional studies (Soeters and van Westen, 1996; Guzzetti et al., 1999)....

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  • ...In the application for landslide susceptibility mapping, the common solution is to create layers of binary values (dummy variables) for each class of an independent parameter (Guzzetti et al., 1999; Lee and Min, 2001; Dai et al., 2001; Dai and Lee, 2002; Ohlmacher and Davis, 2003)....

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  • ...In seeking a susceptibility map, the method adopted in literature is to divide the histogram of the probability map into different categories based on expert opinions (Guzzetti et al., 1999; Lee and Min, 2001; Dai and Lee, 2002; Ohlmacher and Davis, 2003)....

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  • ...The process of creating these maps involves several qualitative or quantitative approaches (Soeters and van Westen, 1996, Aleotti and Chowdhury, 1999; Guzzetti et al., 1999)....

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01 Jan 1996

1,549 citations


"The application of GIS-based logist..." refers methods in this paper

  • ...Examples are the use of the analytic hierarchy process (AHP) of Saaty (1980) by Barredol et al....

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