Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning Algorithm

Chang, Lili; Zhang, Rui; Wang, Chunsheng

doi:10.3390/rs14112717

Cited by 5 publications

(3 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To understand the overall pattern of mass movement distribution and mass movement susceptible areas, we performed summary statistics on the distribution of mass movement in each susceptibility class, distribution of susceptibility classes and the frequency ratio of mass movements in each susceptibility class (Figure 11). In general, models with more historical mass movement points concentrated in predicted high-prone areas are considered to have better performance [98]. From Figure 11, it can be found that for the three types of mass movements, only TBMM's results have more than 80% of the mass movement points concentrated in the areas with high and very high susceptibility (81.6%, 80.4%, and 85.1% for debris flow, rockfall and landslide, respectively), and the least mass movements were classified to low and very low-prone areas (6.9%, 5.8%, and 2.5%, respectively).…”

Section: Spatial Pattern Comparison Of the Susceptibility Mapmentioning

confidence: 99%

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Zhang

2022

Remote Sensing

View full text Add to dashboard Cite

Generating an unbiased inventory of mass movements is challenging, particularly in a large region such as China. However, due to the enormous threat to human life and property caused by the increasing number of mass movements, it is imperative to develop a reliable nationwide mass movement susceptibility model to identify mass movement-prone regions and formulate appropriate disaster prevention strategies. In recent years, the mixed-effects models have shown their unique advantages in dealing with the biased mass movement inventory, yet there are no relevant studies to compare different mixed-effects models. This research compared three mixed-effects models to explore the most plausible and robust susceptibility mapping model, considering the inherently heterogeneously complete mass movement information. Based on a preliminary data analysis, eight critical factors influencing mass movements were selected as basis predictors: the slope, aspect, profile curvature, plan curvature, road density, river density, soil moisture, and lithology. Two additional factors, namely, the land use and geological environment division, representing the inventory bias were selected as random intercepts. Subsequently, three mixed-effects models—Statistical-based generalized linear mixed-effects model (GLMM), generalized additive mixed-effects model (GAMM), and machine learning-based tree-boosted mixed-effects model (TBMM)—were adopted. These models were used to evaluate the susceptibility of three distinct types of mass movements (i.e., 28,814 debris flows, 54,586 rockfalls and 108,432 landslides), respectively. The results were compared both from quantitative and qualitative perspectives. The results showed that TBMM performed best in all three cases with AUROCs (Area Under the Receiver Operating Characteristic curve) of cross-validation, spatial cross-validation, and predictions on simulated highly biased inventory, all exceeding 0.8. In addition, the spatial prediction patterns of TBMM were more in line with the natural geomorphological underlying process, indicating that TBMM can better reduce the impact of inventory bias than GLMM and GAMM. Finally, factor contribution analysis showed the key role of topographic factors in predicting the occurrence of mass movements, followed by road density and soil moisture. This study contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation. Further, it demonstrates the tremendous potential of TBMM for mass movement susceptibility assessment, despite inherent biases in the inventory.

show abstract

Section: Spatial Pattern Comparison Of the Susceptibility Mapmentioning

confidence: 99%

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Zhang

2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Mass movements such as landslides, mudflows, and rockfalls emerge as noteworthy geological hazards, imparting considerable impact on the surrounding environment [1] and anthropogenic, economic and environmental consequences [2]. These phenomena predominantly manifest in orogenic terrains, exemplified by the Bokoya Massif situated to the east of Al-Hoceima city in northern Morocco, constituting our designated study locale.…”

Section: Introductionmentioning

confidence: 99%

Landslide mapping using geospatial techniques: A case study of the Bokoya Massif (Central Rif, Morocco)

Ait Omar,

Taher,

Etebaai

2024

E3S Web Conf.

View full text Add to dashboard Cite

The terrestrial zone of Al-Hoceima National Park is faced with a multitude of natural and man-made hazards that threaten the stability of the soil and the ecological balance: earthquakes, coastal erosion, floods, fires and landslides. The creation of landslide susceptibility maps is a crucial step in making informed decisions for preventing and mitigating landslide risks. This work focuses on the analysis of landslide potential in the Bokoya massif, located in the central Moroccan Rif, using the Analytical Hierarchical Process (AHP). Our aim is to develop a landslide probability map, which will be an important tool for land-use planning and risk management, helping to identify priority areas for further research.

show abstract

“…Oversampling or the superior performance of some algorithms can solve the problem of data imbalance [13,14]. The integrated algorithms can also combat the noise in the data [15,16]. However, they are not easy to implement, especially for non-professional technical personnel.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests

et al. 2022

View full text Add to dashboard Cite

Landslide susceptibility prediction (LSP) is the first step to ease landslide disasters with the application of various machine learning methods. A complete landslide inventory, which is essential but difficult to obtain, should include high-quality landslide and non-landslide samples. The insufficient number of landslide samples and the low purity of non-landslide samples limit the performance of the machine learning models. In response, this study aims to explore the effectiveness of isolated forest (IF) to solve the problem of insufficient landslide samples. IF belongs to unsupervised learning, and only a small share of landslide samples in the study area were required for modeling, while the remaining samples were used for testing. Its performance was compared to another advanced integration model, adaptive boosting integrated with decision tree (Ada-DT), which belongs to two-class classifiers (TCC) and needs a sufficient number of samples. Huangpu District, Guangzhou City, Guangdong Province in China, was selected as the study area, and 13 predisposing factors were prepared for the modeling. Results showed that the IF proved its effectiveness with an AUC value of 0.875, although the Ada-DT model performed better (AUC = 0.921). IF outperformed the Ada-DT model in terms of recognizing landslides, and the sensitivity values of IF and the Ada-DT model were 90.00% and 86.67%, respectively, while the Ada-DT model performed better in terms of specificity. Two susceptibility maps obtained by the models were basically consistent with the field investigation, while the areas predicted by IF tended to be conservative as higher risk areas were presented, and the Ada-DT model was likely to be risky. It is suggested to select non-landslide samples from the very low susceptibility areas predicted by the IF model to form a more reliable sample set for Ada-DT modeling. The conclusion confirms the practicality and advancement of the idea of anomaly detection in LSP and improves the application potential of machine learning algorithms for geohazards.

show abstract

Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning Algorithm

Cited by 5 publications

References 68 publications

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Landslide mapping using geospatial techniques: A case study of the Bokoya Massif (Central Rif, Morocco)

Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests

Contact Info

Product

Resources

About