Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

Kundu, Shyam Sundar; Saha, Aniruddha; Sharma, D. C.; Pant, Charu C.

doi:10.1007/s12524-012-0255-y

Cited by 54 publications

(20 citation statements)

References 29 publications

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“…A coefficient of or very close to 0 indicates a factor not being influential in landslide occurrence. The LR model estimates the coefficients and statistics, based on the values of independent variables and the status of the dependent variable in a sample of data, using a maximum likelihood method [46]. Using the outcomes derived from the implementation of model on the selected sample, the probability of landslide occurrence can be calculated.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

2018

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Abstract:The main purpose of this study is to explore the impact of analysis scale on the performance of a quantitative model for landslide susceptibility assessment through empirical analyses in the northern Peloponnese, Greece. A multivariate statistical model like logistic regression (LR) was applied at two different scales (a regional and a more detailed scale). Due to this scale difference, the implementation of the model was based on two landslide inventories representing in a different way the landslide occurrence (as point and polygon features), and two datasets of similar geo-environmental factors characterized by a different size of grid cells (90 m and 20 m). Model performance was tested by a standard validation method like receiver operating characteristics (ROC) analysis. The validation results in terms of accuracy (about 76%) and prediction ability (Area under the Curve (AUC) = 0.84) of the model revealed that the more detailed scale analysis is more appropriate for landslide susceptibility assessment and mapping in the catchment under investigation than the regional scale analysis.

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Section: Methodsmentioning

confidence: 99%

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

2018

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“…The second one is the commonly-used receiver operating characteristic (ROC) and the area under the ROC curve (AUC). Since a test with perfect discrimination always produces a curve passing through the upper left corner of the plot, the closer the ROC curve is to the upper left corner, the more accurate are the landslide predictive results [83,84]. The AUC value ranges from 0.5-1, and it is close to 1, representing that the model is perfectly reasonable for prediction [85].…”

Section: Objective Evaluation Measuresmentioning

confidence: 98%

Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China

Wang

Niu

et al. 2017

Remote Sensing

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Abstract:In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannon's entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors for each cluster is determined based on the CF values of each factor. Furthermore, the logistic regression model is used as an example of statistical models in each cluster using the selected causative factors for landslide prediction. Finally, a global landslide susceptibility map is obtained by combining the regional maps. Experimental results based on both qualitative and quantitative analysis indicated that the proposed framework can achieve more accurate landslide susceptibility maps when compared to some existing methods, e.g., the proposed framework can achieve an overall prediction accuracy of 91.76%, which is 7.63-11.5% higher than those existing methods. Therefore, the local scale LSM technique is very promising for further improvement of landslide prediction.

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“…In 1980s, 1990s and early 2000s, a number of authors used LHZ mapping (Gupta and Joshi 1990;Gupta and Anbalagan 1997;Nagarajan et al 1998;Saha et al 2002). Another term 'landslide susceptibility' , in this context was given as spatial probability of occurrence of landslides based on a set of geo-environmental factors (Brabb 1984;Sarkar and Kanungo 2004;Lee and Sambath 2006;Kundu et al 2013;Kayastha et al 2013). Some authors are using the term 'landslide hazard mapping' in accordance with the definition of natural hazard given by UNO.…”

Section: Introductionmentioning

confidence: 99%

“…Several quantitative and semiquantitative techniques were applied for landslide susceptibility/hazard modelling in Himalayan terrain. Logistic regression technique for data integration of geoenvironmental factors (Das et al 2010), empirical modelling of landslide susceptibility in the Darjeeling Himalayas (Ghosh et al 2011) and several others (Das et al 2012;Kayastha et al 2013;Kundu et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim

Anbalagan

Kumar

Lakshmanan

et al. 2015

GEOENVIRON DISASTERS

116

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Background: Sikkim Himalaya is under consistent distress due to landslides. Abrupt thrust on infrastructure development in the valley regions of Sikkim Himalaya has led to a need for a prior planning to face landslide hazard. A comprehensive study for the identification of landslide hazard zones using landslide frequency ratio and fuzzy logic in GIS environment has been presented for the Lachung valley, Sikkim, India, where a number of hydroelectric projects are proposed.Temporal remote sensing data was used to generate significant landslide causative factors in addition to landslide inventory. Primary topographic attributes namely slope, aspect and relative relief were derived from digital elevation model. Landslide frequency ratio approach was adopted to correlate landslide causal factors with landslide incidences. Further, fuzzy logic method was used for the integration of landslide causative factors in order to delineate the landslide hazard zones. Fuzzy memberships were derived from the landslide frequency ratio values. Different gamma values were used in fuzzy gamma integration process, which resulted different landslide hazard index maps. Receiver operating characteristic curves were prepared to analyze consistency of the resulting landslide hazard index maps.

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Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

Cited by 54 publications

References 29 publications

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China

Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim

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