Detection and segmentation of loess landslides via satellite images: a two-phase framework

Li, Huajin; He, Yusen; Liu, Fangzhou; Deng, Jiahao; Li, Weile; Wei, Yong

doi:10.1007/s10346-021-01789-0

Cited by 111 publications

(74 citation statements)

References 34 publications

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“…It is a crucial pre-requisite phase to build a trustworthy and accurate landslide inventory map for landslide analysis (Guzzetti et al, 1999;Li et al, 2022;Tanyas and Lombardo, 2020). In this study, the imagery for interpretation was chosen based on the year of acquisition, coverage, minimum of clouds, and resolution to detect multi-temporal landslides.…”

Section: Multi-temporal Landslide Inventorymentioning

confidence: 99%

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Luo

Pei

Zhong

et al. 2022

Front. Environ. Sci.

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The 2017 Mw 6.5 Jiuzhaigou earthquake (Sichuan, China) is the first strong ground motion that struck the famous world heritage site, causing widespread landslides and severe rock mass damage effects and landscapes undergoing rapid evolution in the Jiuzhaigou National Geopark. However, the understanding of the variability of pre- and post-earthquake landslide susceptibility and landslide conditioning factor effects over time remains limited. This study aims to carry out multi-temporal statistical landslide susceptibility modeling at the slope-unit level related to this event. To achieve this, we initially used a set of remote sensing imageries in GIS to obtain systematic landslide inventories across the pre-, co-, and post-seismic periods. Based on three landslide inventory datasets, we developed three statistical models by incorporating 14 landslide conditioning (seismic, topographic, and geologic) factors into a binary logistic regression (BLR) model. Finally, we utilized the area under the receiver operating characteristic (AUC) (QA) curve to assess each model’s calibration and validation performance. The results show that the BLR model has good prediction applicability for both normal and seismic landslides in the study area with outstanding to excellent predictive accuracy for Mod1 (pre-seismic, AUC = 0.801), Mod2 (co-seismic, AUC = 0.942), and Mod3 (post-seismic, AUC = 0.880) periods. There are variations in both the importance of landslide conditioning factors and susceptibility maps through time, and the number of slope units with a mean probability over 0.8 from only one (pre-seismic) increased to 21 (post-seismic). The dynamic susceptibility maps are of great significance for identifying potentially unstable slopes and providing references for hazard and risk assessment, which could provide new insights into geo-environmental protection and regional landslide evaluation in scenery spots, even for those world heritage sites in the tectonic active mountainous region. Moreover, more frequent or extended observation periods could contribute a further understanding of the post-seismic landslide developments in the Jiuzhaigou area.

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Section: Multi-temporal Landslide Inventorymentioning

confidence: 99%

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Luo

Pei

Zhong

et al. 2022

Front. Environ. Sci.

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“…Concurrently, many articles studied tunnel collapse by applying the modeling experiment (Barla, 2008;Yang et al, 2019;Liu et al, 2020;Hao et al, 2021;Wang et al, 2021;Lan et al, 2022;Li et al, 2022). The simulation experiment allows for a more intuitive understanding of the structure's failure process and a detailed investigation of its failure mechanism.…”

Section: Introductionmentioning

confidence: 99%

Mechanism and Sensitivity Analysis of Collapse in Large Section Mountain Neighborhood Tunnels

et al. 2022

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Collapse is one of the most frequent geological disasters in mountain tunnel engineering, posing major safety concerns for underground structures and construction crews. According to a catastrophic collapse in the shallow buried area of the Huangjiakuang tunnel in Weihai City, Shandong Province, the contributing factors, that is, the surrounding rock property, influence of neighboring tunnel construction, and tunnel over-excavation are systematically investigated. The tunnel collapse, breaks through the ground surface, is inverted using MIDAS GTS NX. A tunnel deformation analysis model is created using the quantitative methods of grey relation analysis and entropy weight methods based on crown settlement and lateral wall horizontal contraction during tunnel excavation. The surrounding rock property, the distance between the left and right tunnel faces, and the over-excavation height have a significant effect on tunnel deformation, which are quantitatively interpreted using the tunnel deformation analysis model. This study is valuable for the multi-factor analysis of tunnel deformation and determining the main contributing factors to the collapse quantitatively.

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“…Basic deep learning algorithms include deep belief networks (Ouyang et al, 2019a), convolutional neural network (LeCun and Bengio 1995), deep neural network (DNN) (Hu et al, 2016), long short-term memory recurrent neural network (LSTM-RNN) (Kong et al, 2017), and stacked extreme learning machine (Huang et al, 2011). The applications of deep learning approaches in forecasting tasks include deterministic forecasting methods, deep-learned feature extraction, error post-processing, and network structure optimization (He et al, 2017a;Li et al, 2018;Li et al, 2022;Ouyang et al, 2018;Ouyang et al, 2019b;Xu et al, 2019;Ahmad et al, 2021;Hrnjica et al, 2021;Tang et al, 2021). The main challenge in using deep learning techniques is to construct the most fitting prediction model for a particular dataset, such as the nitrate concentration in this study.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Modeling of Stream Nitrate Concentration and Transportation via IFPA-ESN and Turning Point Analyses

Tang

Deng

Zang

et al. 2022

Front. Environ. Sci.

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Increased concentrations of nitrogenous compounds in stream networks are detrimental to the health of both humans and ecosystems. Monitoring, modeling, and forecasting nitrate concentration in the temporal domain are essential for an in-depth understanding of nitrate dynamics and transformation within stream networks. In this study, an advanced chaotic modeling and forecasting approach integrated with turning point analysis is proposed. First, the time-series daily nitrate concentrations in the form of nitrate-nitrite were reconstructed based on the chaotic characteristics and then input into the forecasting models. Second, an echo state network (ESN) was developed for one-day-ahead nitrate concentration forecasting, and the hyperparameters were optimized through an improved flower pollination algorithm (IFPA) to achieve a high efficiency. Furthermore, turning point analysis was performed to quantify the relationship between discharge and peak nitrate concentration. The Ricker function was fitted, and the parameters were estimated for turning points using the forecasted nitrate concentration and measured discharge. Field data, including daily stream nitrate concentration and information on discharge collected from eight different monitoring sites in the southern Sichuan Basin, China, were utilized for case studies. A comparative analysis was performed under three modeling scenarios, viz. conventional time-series modeling, temporal signal decomposition, and data reconstruction and embedding with chaotic characteristics. Four benchmark time-series forecasting algorithms were compared against the proposed IFPA-ESN in the above-mentioned scenarios. For each site, parameters of the Ricker functions were estimated, and turning points were computed based on the forecasted nitrate concentration and discharge. Computational results validated the superiority of the proposed approach in improving the accuracy of stream nitrate concentration prediction. The limitations to the supply and transportation of nitrogenous compounds were quantified, which would be valuable for pollution mitigation in the future.

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Detection and segmentation of loess landslides via satellite images: a two-phase framework

Cited by 111 publications

References 34 publications

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Mechanism and Sensitivity Analysis of Collapse in Large Section Mountain Neighborhood Tunnels

Chaotic Modeling of Stream Nitrate Concentration and Transportation via IFPA-ESN and Turning Point Analyses

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