Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach Article Swipe

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Tran Van Phong , Hai‐Bang Ly , Phan Trọng Trịnh , Indra Prakash ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.15625/0866-7187/42/3/15047

Landslide susceptibility mapping is a helpful tool for assessment and management of landslides of an area. In this study, we have applied first time Forest by Penalizing Attributes (FPA) algorithm-based Machine Learning (ML) approach for mapping of landslide susceptibility at Muong Lay district (Vietnam). For this aim, 217 historical landslides locations were identified and analyzed for the development of FPA model and generation of susceptibility map. Nine landslide topographical and geo-environmental conditioning factors (curvature, geology/lithology, aspect, distance from faults, rivers and roads, weathering crust, slope, and deep division) were utilized to construct the training and validating datasets for landslide modeling. Different quantitative statistical indices including Area Under the Receiver Operating Characteristic (ROC) curve (AUC) were used to evaluate the performance of the model. The results indicate that the predictive capability of the FPA is very good for landslide susceptibility mapping on both training (AUC = 0.935) and validating (AUC = 0.882) datasets. Thus, the novel FPA based ML model can be utilized for the development of accurate landslide susceptibility map of the study area and this approach can also be applied in other landslide prone areas.

Related Topics

Landslide

Receiver Operating Characteristic

Concepts

Landslide Lithology Geology Receiver operating characteristic Data mining Algorithm Remote sensing Cartography Computer science Machine learning Geomorphology Geography Paleontology

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Type: article
Language: en
Landing Page: https://doi.org/10.15625/0866-7187/42/3/15047
PDF: https://vjs.ac.vn/index.php/jse/article/download/15047/pdf
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Cited By: 27
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DOI: https://doi.org/10.15625/0866-7187/42/3/15047

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Title: Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach

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Language: en

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Publication year: 2020

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Publication date: 2020-06-23

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Authors: Tran Van Phong, Hai‐Bang Ly, Phan Trọng Trịnh, Indra Prakash

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Landing page: https://doi.org/10.15625/0866-7187/42/3/15047

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Concepts: Landslide, Lithology, Geology, Receiver operating characteristic, Data mining, Algorithm, Remote sensing, Cartography, Computer science, Machine learning, Geomorphology, Geography, Paleontology

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Cited by: 27

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sustainable_development_goals[0].id	https://metadata.un.org/sdg/15
sustainable_development_goals[0].score	0.5899999737739563
sustainable_development_goals[0].display_name	Life in Land
citation_normalized_percentile.value	0.96477271
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True