Probabilistic landslide susceptibility mapping along Tipri to Ghuttu highway corridor, Garhwal Himalaya (India)

Pandey, Vijendra Kumar; Sharma, Milap Chand

doi:10.1016/j.rsase.2017.07.007

Cited by 21 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the results of the importance analysis, the distance to rivers (AM = 0.378) is the most important landslide influencing factor in the study area. The results of the Bel value are similar to those found in previous studies, with a higher probability of landslides near the river area [114]. Different geomorphic parts of the river cause different external forces and stress distributions on the slope, so the failure modes of the slope are also different.…”

Section: Discussionsupporting

confidence: 87%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

View full text Add to dashboard Cite

In this study, Random SubSpace-based classification and regression tree (RSCART) was introduced for landslide susceptibility modeling, and CART model and logistic regression (LR) model were used as benchmark models. 263 landslide locations in the study area were randomly divided into two parts (70/30) for training and validation of models. 14 landslide influencing factors were selected, such as slope angle, elevation, aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to road, soil, normalized difference vegetation index (NDVI), land use, and lithology. Finally, the hybrid RSCART model and two benchmark models were applied for landslide susceptibility modeling and the receiver operating characteristic curve method is used to evaluate the performance of the model. The susceptibility is quantitatively compared based on each pixel to reveal the system spatial pattern between susceptibility maps. At the same time, area under ROC curve (AUC) and landslide density analysis were used to estimate the prediction ability of landslide susceptibility map. The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model.Water 2020, 12, 113 2 of 29 weights of evidence [10][11][12], frequency ratio [13][14][15][16][17], logistic regression [18][19][20][21], linear multivariate regression, multivariate adaptive regression spline [22][23][24], and statistical index [25,26] have been widely used. However, these traditional statistical methods do not provide satisfactory evaluation of the correlation between landslide influencing factors [4,27].Therefore, machine learning technologies have drawn extensive attention, and many kinds of machine learning methods have been developed and used, such as classification and regression trees [28,29], adaptive neuro-fuzzy inference systems [30,31], fuzzy logic [32,33], alternating decision trees [34][35][36], support vector machine [37][38][39], artificial neural networks [40,41], and random forest [4,[42][43][44][45]. In particular, hybrid models are increasingly used, such as the rotation forest-based decision trees [46,47], frequency ratio-based ANFIS model [48], bagging-based reduced error pruning trees [49], and multiboost-based support vector machines [50].Spatial prediction of landslide is not only the first important step, but also one of the most difficult tasks [31,51]. Many modeling methods have been used in the construction of landslide susceptibility maps in the past, but the accuracy of these models has not been accepted by all researchers [52]. Youssef et al. used CART model to study landslide susceptibility in Wadi Tayyah Basin. The result of post-cart model is not optimal [53]. However, when Felicísimo et al. studied the landslide susceptibility, CART shows...

show abstract

Section: Discussionsupporting

confidence: 87%

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Chen

2019

Water

View full text Add to dashboard Cite

show abstract

“…Loss of vegetation accentuates the flooding process. Topography and river density affect the occurrence of flash flood [109]. Considering this, ten factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were used to construct the flood database for modelling.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

109

View full text Add to dashboard Cite

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

show abstract

“…Topographic features belong to the most essential features for landslide susceptibility and hazard mapping and are therefore used in almost every study, e.g. Pandey and Sharma (2017); Liu et al (2021) ;Conforti et al (2014); Kuradusenge et al (2020). While elevation and geomorphological parameters of the surface can be obtained from various sources, digital elevation models (DEMs) are most commonly used.…”

Section: Geospatial Datasetsmentioning

confidence: 99%

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

Edrich,

Yildiz,

Roscher

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine learning (ML) has become increasingly popular in recent years for susceptibility and hazard mapping tasks. Necessary steps of ML-based workflows for various purposes in the context of geohazards are implemented repeatedly for new studies. To facilitate an effective, modular and extensible application of ML for susceptibility and hazard mapping, a generic framework based on a Random Forest (RF) classifier is presented herein. It supports especially data pre-processing and RF input dataset generation, as well as the susceptibility or hazard map generation adapted to an individual research question. The FAIR data principles were adopted as a guideline during the design and implementation of the framework. In addition to the reproducibility of the resulting binary maps, the implementation enables extensibility and scalability. The framework is applied to the test case of rainfall triggered shallow landslide susceptibility in canton Grisons, Switzerland. Computational experiments investigating the influence of the composition of the training dataset on the mapping result are conducted. Thereby the combination of features, sampling strategies for absence (non-landslide) locations, and transferability from local to regional scale were of interest. The results emphasise the importance of considering the composition of training data for the quality of the model and as a result also the reliability of the generated map.

show abstract

Probabilistic landslide susceptibility mapping along Tipri to Ghuttu highway corridor, Garhwal Himalaya (India)

Cited by 21 publications

References 33 publications

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

Contact Info

Product

Resources

About