2015
DOI: 10.4081/jae.2015.450
|View full text |Cite
|
Sign up to set email alerts
|

Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo municipality)

Abstract: Although landslides are frequent natural phenomena in mountainous regions, the lack of data in emerging countries is a significant issue in the assessment of shallow landslide susceptibility. A key factor in risk-mitigation strategies is the evaluation of deterministic physical models for hazard assessment in these data-poor regions. Given the lack of physical information, input parameters to these data-intensive deterministic models have to be estimated, which has a negative impact on the reliability of the a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
4
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(5 citation statements)
references
References 49 publications
1
4
0
Order By: Relevance
“…This approach allowed us to filter down to 10 influential factors effective across all models, enhancing prediction accuracy as evidenced by improved AUC and DCA plots (Figures 8 and 9). This method not only confirmed the individual model findings but also demonstrated superior stability and predictive power across the regional dataset, aligning with the successes reported in [18,28].…”
Section: The Optimization Of the Evaluation Factors Of The Seismic La...supporting
confidence: 83%
See 1 more Smart Citation
“…This approach allowed us to filter down to 10 influential factors effective across all models, enhancing prediction accuracy as evidenced by improved AUC and DCA plots (Figures 8 and 9). This method not only confirmed the individual model findings but also demonstrated superior stability and predictive power across the regional dataset, aligning with the successes reported in [18,28].…”
Section: The Optimization Of the Evaluation Factors Of The Seismic La...supporting
confidence: 83%
“…Therefore, the selection of crucial evaluation factors and reduction in redundant factors in the dataset can not only resolve the fitting problem of machine learning but also reduce the computational burden and improve the efficiency of the model. To enhance the model's accuracy and address potential overfitting issues in machine learning-based models, methodologies such as frequency ratio [27], deterministic factor [28], Pearson correlation coefficient [29], factor analysis [30], rough set [31], information gain [32], and recursive feature elimination [33] were employed.…”
Section: Introductionmentioning
confidence: 99%
“…A small amount of water (0.06 mm/h) is allowed to be discharged at the foot of the slope. The yield criterion of slope soil adopts the modified Cambridge model [46]. The shape of the yield surface of the modified Cambridge model is defined by the following formula:…”
Section: Shallow Slope Stability Analysismentioning
confidence: 99%
“…Furthermore, they remain simple enough to allow calibration of their factors and validation of their results using existing landslide inventories. One of these terrain stability models, called SINMAP (Stability INdex MAPping) has been tested under different geological and hydrological conditions by several authors (Morrissey, Wieczorek and Morgan, 2001;Zaitchik and Van Es, 2003;Calcaterra, de Risso and di Martire, 2004;Silva, 2006;Tarolli and Tarboton, 2006;Meisina and Scarabelli, 2007;Lopes et al, 2007;Nery and Vieira, 2012;Michel, Kobiyama and Goerl, 2014;Preti and Letterio, 2015;Terhorst and Jaeger, 2015;Abascal and González Bonorino, 2015;Rabonza et al, 2016) and it has proved to be highly reliable in predicting slope instabilities. The consistency of SINMAP has been tested by Zizioli et al (2013) who have compared its performance against other models as SHALSTAB, TRIGRS, and SLIP, resulting in a similar global accuracy for all models.…”
Section: Introductionmentioning
confidence: 99%