Apply two hybrid methods on the rainfall-induced landslides interpretation

Chang, Kuan-Tsung; Hwang, Jin-Tsong; Liu, Jin-King; Wang, Edward-Hua; Wang, Chu-I

doi:10.1109/geoinformatics.2011.5980950

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…Individual landslide polygons now can be outlined successfully using Object‐Based Image Analysis (K. ‐T. Chang, Hwang, et al, ), although problems of separating merged landslide deposits remain. Spectral, topographic, and shape metrics also can characterize EQTLs according to their type (Martha et al, ).…”

Section: Coseismic Landslidesmentioning

confidence: 99%

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Fan

Scaringi

Korup

et al. 2019

Reviews of Geophysics

658

325

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Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

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Section: Coseismic Landslidesmentioning

confidence: 99%

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Fan

Scaringi

Korup

et al. 2019

Reviews of Geophysics

658

325

View full text Add to dashboard Cite

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“…ML algorithms that have been used for landslide prediction include support vector machine [20,30,47,69,95], artificial neural network [31,86], decision trees such as naïve Bayes tree (NBT) [22,85], radial basis function (RBF) [38], kernel logistic regression (KLR) [13,21], Bayes' net (BN) [19], bivariate statistical index (SI) [23], stochastic gradient descent (SGD) [94], particle swarm optimization (PSO) [18], best-first decision tree (BFDT) [24], random subspacebased support vector machines (RSSVM) [39] and logistic model tree (LMT) [20]. Ensemble models have been used in landslide susceptibility mapping due to their novelty and their ability to comprehensively asses landsliderelated parameters for discrete classes of independent factor [15,16,25,44,49,63,67,87,93] . To achieve this, a frequency ratio (FR) and logistic regression (LR) models have been applied to obtain maps of landslide susceptibility (spatial prediction) using the ArcGIS software (version 10.2) for the area.…”

Section: Introductionmentioning

confidence: 99%

Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India

Das

Lepcha

2019

SN Appl. Sci.

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Landslide is one of the important disasters taking place on earth, which may be either a natural or man-made process. Landslides are more active and disastrous in hilly and mountainous regions. The present study aims to identify the landslide susceptibility areas in the Relli river basin in Darjeeling Himalaya using logistic regression (LR) and frequency ratio (FR) models. The GIS techniques have been used for landslide susceptibility mapping. A total number of 67 landslide locations have been identified from Google Earth images and multiple field surveys. 70% of landslide locations have been randomly selected and used as training data set for preparing landslide susceptibility map, and the remaining 30% have been used as validation data set. For the present study, 20 different factors like drainage density, drainage texture, infiltration number, stream frequency, stream junction frequency, stream power index, lithology, soil, relative relief, slope, maximum relief, drainage intensity, ruggedness number, rainfall, dissection index, aspect, relief class, and distance from stream, topographic wetness index and land use land cover have been used. The application of the logistic regression and frequency ratio model has demonstrated that the lower catchment of the basin has been widely dominated by the most landslide susceptibility areas than other parts of the catchment. Almost 6.92 sq km (4.05%) and 7.44 sq km (4.36%) areas out of 170.61 sq km area of the basin have been observed as very high and high landslide susceptibility categories, respectively, for FR model and 5.75 sq km (3.37%) and 1.86 sq km (1.09%) areas have been under very high and high landslide susceptibility zones for LR model. Finally, the ROC curve has been used to validate the models. The prediction capabilities of the models seem significant as the area under the curve value ranges from 75 to 81%.

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“…The SVM and ML classifiers are widely used in the remote sensing community and well employed in research literatures for the land cover classification [10], [18], [26], [28], [32]- [35], [43], [50]. Furthermore, the landslide classifications reported in [18], [34], and [35] have also been studied by the NN, SVM, and ML methods. These five classifiers were all compared to the FCNFS in the experiments.…”

Section: B Comparison Of Resultsmentioning

confidence: 99%

“…For the purposes of classification, the nearest neighbor (NN)-based pattern classifier has been implemented and demonstrated [34], [35]. In order to solve the problem of a limited number of training samples in the NN method, the nearest linear combination (NLC) utilized as a template-based approach was developed [36].…”

mentioning

confidence: 99%

Multisource Data Fusion and Fisher Criterion-Based Nearest Feature Space Approach to Landslide Classification

Chang

Wang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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International audienceIn this paper, a novel technique known as the Fisher criterion-based nearest feature space (FCNFS) approach is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. The method is developed for land cover classification based upon the fusion of remotely sensed images of the same scene collected from multiple sources. This paper presents a framework for data fusion of multisource remotely sensed images, consisting of two approaches: 1) the band generation process(BGP); and 2) the FCNFS classifier. We propose the BGP to create a new set of additional bands that are specifically accommodated to the landslide class and are extracted from the original multisource images. In comparison to the original nearest feature space (NFS) method, the proposed improved FCNFS classifier uses the Fisher criterion of between-class and within-class discrimination to enhance the classifier. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a preprocessing step of the NFS method. After completion of the training, the classification results can be obtained from the NFS algorithm. In order for the proposed FCNFS to be effective for multispectral images, a multiple adaptive BGP is introduced to create an additional set of bands specially accommodated to landslide classes. Experimental results show that the proposed BGP/FCNFS framework is suitable for land cover classification in Earth remote sensing and improves the classification accuracy compared to conventional classifiers

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Apply two hybrid methods on the rainfall-induced landslides interpretation

Cited by 7 publications

References 4 publications

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India

Multisource Data Fusion and Fisher Criterion-Based Nearest Feature Space Approach to Landslide Classification

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