Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya

Pandey, Vijendra Kumar; Pourghasemi, Hamid Reza; Sharma, Milap Chand

doi:10.1080/10106049.2018.1510038

Cited by 85 publications

(30 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the AUPRC, accuracy, and kappa results, ME was shown to have the best overall performance. This reflects similar findings in [98][99][100][101]. ME exceeds other ML models because it uses search-based optimization to determine the relative importance of factors [101].…”

Section: Discussionsupporting

confidence: 72%

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

show abstract

Section: Discussionsupporting

confidence: 72%

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have shown that the SVM algorithm has the highest goodness-of-fit and prediction accuracy of the three machine learning algorithms tested in this study based on both training and validation datasets. This result is consistent with the findings of other landslide researchers [22,[112][113][114][115][116][117]. For example, Kalantar et al [115], compared the performance of SVM, LR, and ANN for landslide assessment in a catchment in the Dodangeh watershed, Mazandaran province, Iran.…”

Section: Discussionsupporting

confidence: 87%

Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

et al. 2020

View full text Add to dashboard Cite

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

show abstract

“…The Maximum Entropy model is a statistical-probabilistic machine learning method, which has been applied to many different fields, such as environmental sciences for predicting species distribution, (being proposed for the first time by Phillips et al, 2004, for the case when just presence data is available in order to calculate the best positive distribution and used with a great accomplishment by Pearson et al, 2007, Elith et al, 2011Bajat et al, 2011;Yang et al, 2013;Muscarella et al, 2014;Cao et al, 2016), groundwater mapping (Rahmati et al, 2016;Wahyudi et al, 2012), crop planting regionalization (Franklin, 2009;Yu et al, 2014;Jaime et al, 2015). Recently, this method was applied with a great success in landslide susceptibility mapping: Liu et al, 2012, Reza et al, 2012Vorpahl et al;2012;Pourghasemi et al, 2012a;Felicísimo et al;Park, 2015;Davis and Blesius, 2015;Moosavi and Niazi, 2016;Lombardo et al, 2016;Kornejady et al, 2017. Pandey et al, 2018 investigated in India the territory along the state highway corridor Tipari to Ghuttu.…”

Section: Methodsmentioning

confidence: 99%

Landslide susceptibility assessment using the maximum entropy model in a sector of the Cluj–Napoca Municipality, Romania

2018

View full text Add to dashboard Cite

The administrative territory of Cluj–Napoca, due to its specific geological and geomorphological characteristics and anthropic activities, has been affected for a long time by landslides. Thus, it becomes necessary to analyse affected areas with different spatial methods, with the aim of generating landslide susceptibility maps. In this research, we studied the most prone area of the city, the Becaș stream watershed, situated in the Southern part of the municipality. The aim of this paper is to generate a valid susceptibility map, to be able to raise awareness about the existing situation: due to human induced activities and rapid urban growth, the peripheral part of Cluj–Napoca becomes more and more prone to mass–movements. We used the maximum entropy (MaxEnt) model, which was fed with accurate information on the existing landslides and seven landslide–causing factors: slope, aspect, land–use, depth of fragmentation, geology and plan– and profile curvature. The results confirm that the most influential factors are the land use and slope–angle, affected in a large degree by human activities. The accuracy of the generated map was verified using the AUC method, proving a very good performance (0.844) of theapplied model.

show abstract

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya

Cited by 85 publications

References 57 publications

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

Landslide susceptibility assessment using the maximum entropy model in a sector of the Cluj–Napoca Municipality, Romania

Contact Info

Product

Resources

About