Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping

Zhu, A‐Xing; Miao, Yuxin; Yang, Lin; Bai, Shibiao; Liu, Junzhi; Hong, Haoyuan

doi:10.1016/j.catena.2018.07.012

Cited by 84 publications

(43 citation statements)

References 80 publications

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“…All of the factors' pixel sizes were set to 30 × 30 m using the Fishnet tool in ArcGIS 10.6, regardless of the initial data format. Among the influencing factors, topography has an extremely important influence on the soil moisture and groundwater and influences slope stabilities [1]. In this study, DEM with a 30 × 30 m pixel size was used from Advanced Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model (ASTER DEM) data jointly developed by METI, Japan, and NASA, the USA provided by the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences in 2019 [41].…”

Section: Landslides Influencing Factorsmentioning

confidence: 99%

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Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Rong

Han

et al. 2020

Water

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Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

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Section: Landslides Influencing Factorsmentioning

confidence: 99%

“…Among the most frequent natural disasters, landslides often result in several casualties and huge economic losses, seriously affecting social development and land use [1,2]. In 2016, 9710 landslides occurred in China, causing 370 deaths and approximately USD 457 million in direct economic losses [3].…”

Section: Introductionmentioning

confidence: 99%

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Rong

Han

et al. 2020

Water

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“…Nearly 100 factors were summarized to reveal the commonly used factors. In accordance with the relevant studies [29,41,42] and the study area, we select eight influencing factors: elevation, topography, slope, lithology, distance to a structure line, distance to a river, average annual rainfall, and distances to roads. Table 1 lists all the DEM data for the study area and their sources.…”

Section: Influencing Factorsmentioning

confidence: 99%

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Wang

Han

et al. 2020

Applied Sciences

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Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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“…php). Normalized difference vegetation index (NDVI) (Chen et al 2019a), normalized difference water index (NDWI) (Luo et al 2019), and bare soil index (BI) (Huang et al 2019) were derived from the Landsat TM 8 image data, resampled with a 10 m resolution (Zhu et al 2018). The geological factors were derived from the 1:200,000 scale geological map, which was obtained from the Geological Survey of Japan, AIST (https://www.gsj.jp/en/).…”

Section: Spatial Data Settingmentioning

confidence: 99%

An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

Nam

Wang

2020

Geoenviron Disasters

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Background: Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme rainfall. Results: The prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%). Conclusions: This study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

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Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping

Cited by 84 publications

References 80 publications

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

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