Revisiting spatiotemporal evolution process and mechanism of a giant reservoir landslide during weather extremes

Engineering Geology, Journal Year: 2024, Volume and Issue: 332, P. 107480 - 107480

Published: March 21, 2024

Language: Английский

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Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2394 - 2394

Published: June 29, 2024

The accurate prediction of landslide susceptibility relies on effectively handling absence samples in machine learning (ML) models. However, existing research tends to generate these feature space, posing challenges field validation, or using physics-informed models, thereby limiting their applicability. rapid progress interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy for mapping the Badong–Zigui near Three Gorges Reservoir, China. We achieve employing a Small Baseline Subset (SBAS) InSAR annual average ground deformation. Subsequently, select from slopes very slow Logistic regression, support vector machine, random forest models demonstrate improvement when samples, indicating enhanced accuracy reflecting non-landslide conditions. Furthermore, compare different integration methods integrate into ML including sampling, joint training, overlay weights, combination, finding that utilizing all three simultaneously optimally improves

Language: Английский

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Characterization of natural fracture development in coal reservoirs using logging machine learning inversion, well test data and simulated geostress analyses

Engineering Geology, Journal Year: 2024, Volume and Issue: 341, P. 107696 - 107696

Published: Aug. 26, 2024

Language: Английский

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Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 2947 - 2947

Published: Aug. 12, 2024

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution research publication trends, (3) progress of algorithms, (4) application techniques models for landslide susceptibility mapping, detections, prediction, inventory deformation monitoring, assessment, extraction management. selections were based on keyword searches using title/abstract keywords from Web Science Scopus. A total 186 articles published between 2011 2024 critically reviewed to provide answers questions related the recent advances use technologies combined with artificial intelligence (AI), machine (ML), deep (DL) algorithms. review revealed that these methods have high efficiency detection, hazard mapping. few current issues also identified discussed.

Language: Английский

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Earthquake Damage Susceptibility Analysis in Barapani Shear Zone Using InSAR, Geological, and Geophysical Data

Geosciences, Journal Year: 2025, Volume and Issue: 15(2), P. 45 - 45

Published: Feb. 1, 2025

The identification of areas that are susceptible to damage due earthquakes is utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. present study emphasized the integration Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data investigate earthquake susceptibility Barapani Shear Zone (BSZ) region We used MintPy v1.5.1 (Miami INsar Timeseries software PYthon) on OpenSARLab platform derive time series using Small Baseline Subset (SBAS) technique. integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, historical records InSAR weighted overlay analysis revealed distinct patterns ground across Zone, higher northern part lower southern part. values ranged from 6 mm/yr about 18 BSZ. Earthquake mapping identified prone event earthquakes. indicated 46.4%, 51.2%, 2.4% area were low, medium, high-susceptibility zones zone. velocity validated Global Positioning System (GPS) region, which a good correlation (R2 = 0.921; ANOVA p-value 0.515). Additionally, field survey suggested evidence intense highly approach enhances our scientific understanding regional tectonic dynamics, mitigating risks enhancing community resilience.

Language: Английский

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Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 17, 2024

Non-landslide samples influence the outcomes of landslide susceptibility assessment. Existing studies did not fully consider equilibrium between and non-landslide in similar environments, resulting poor reliability This study proposed a optimization method with constraint disaster-pregnant environment similarity to construct balanced samples. We employed heterogeneous stacking blending ensemble learning models generate focused on Bailong River Basin complex frequent landslides as area. First, we extracted 12 influencing factors based multiple sources analyzed spatial distribution patterns landslides. Second, constructed environments assessment units obtained from curvature watershed selected an equal amount both every different environment. Finally, three classic neural network models, namely multilayer perceptron, convolutional network, gated recurrent unit were used base for assess susceptibility. The findings suggested that results optimized more reliable, especially improved prediction sample-sparse regions. this demonstrated highest area under curve 0.88 testing dataset, outperforming models. issue unreliable regions within can be effectively addressed by considering sampling environments.

Language: Английский

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Analysis of the impact of terrain factors and data fusion methods on uncertainty in intelligent landslide detection

Landslides, Journal Year: 2024, Volume and Issue: 21(8), P. 1849 - 1864

Published: April 16, 2024

Language: Английский

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Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 177, P. 106058 - 106058

Published: May 2, 2024

Language: Английский

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Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction

Water, Journal Year: 2024, Volume and Issue: 16(15), P. 2152 - 2152

Published: July 30, 2024

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms predicting displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags selection. This study investigates the impact selection techniques performance ML algorithms prediction. The Shuping Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent algorithms. Both static models, including backpropagation neural network (BPNN), support vector (SVM), dynamic long short-term memory (LSTM), gated recurrent unit (GRU), included. Each model evaluated under three techniques: raw multivariate time series, maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). results that appropriate methods could significantly improve models. In contrast, models effectively leverage inherent capabilities capturing dynamics within seeing marginal gains with extensive compared no strategy. optimal approach varies based specific landslide, highlighting importance case-specific assessments. findings this offer guidance integrating maximize robustness generalizability data-driven frameworks.

Language: Английский

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Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Engineering Geology, Journal Year: 2024, Volume and Issue: 341, P. 107690 - 107690

Published: Aug. 22, 2024

Language: Английский

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