Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Geoscience Frontiers, Год журнала: 2024, Номер 15(6), С. 101890 - 101890

Опубликована: Июль 9, 2024

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding rely relatively static environmental conditions, which exposes the hysteresis refined-scale temporal dynamic changes. This study presents an improved approach by integrating machine learning models based random forest (RF), logical regression (LR), gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology comparing them to their respective original models. The results demonstrated that combined improves prediction accuracy reduces false negative positive errors. LR-InSAR model showed best performance at both regional smaller scale, particularly when identifying areas high very susceptibility. Modeling were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. great significance accurately assess help reduce prevent risk.

Язык: Английский

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Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер 16(8), С. 3192 - 3205

Опубликована: Март 16, 2024

Landslide susceptibility mapping is an integral part of geological hazard analysis. Recently, the emphasis many studies has been on data-driven models, notably those derived from machine learning, owing to their aptitude for tackling complex non-linear problems. However, prevailing models often disregard qualitative research, leading limited interpretability and mistakes in extracting negative samples, i.e. inaccurate non-landslide samples. In this study, Scoops 3D (a three-dimensional slope stability analysis tool) was utilized conduct a assessment Yunyang section Three Gorges Reservoir area. The depth bedrock predicted utilizing Convolutional Neural Network (CNN), incorporating local boreholes building insights prior research. Random Forest (RF) algorithm subsequently used execute landslide proposed methodology demonstrated notable increase 29.25% evaluation metric, area under receiver operating characteristic curve (ROC-AUC), outperforming benchmark model. Furthermore, map generated by model superior interpretability. This result not only validates effectiveness amalgamating mathematical mechanistic such analyses, but it also carries substantial academic practical implications.

Язык: Английский

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Rainfall and water level fluctuations dominated the landslide deformation at Baihetan Reservoir, China

Journal of Hydrology, Год журнала: 2024, Номер 642, С. 131871 - 131871

Опубликована: Авг. 22, 2024

Язык: Английский

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Impacts of cascade dam construction on riparian vegetation in an alpine region

Journal of Hydrology, Год журнала: 2025, Номер 652, С. 132665 - 132665

Опубликована: Янв. 4, 2025

Язык: Английский

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Delineating geological structure utilizing integration of remote sensing and gravity data: a study from Halmahera, North Molucca, Indonesia

VIETNAM JOURNAL OF EARTH SCIENCES, Год журнала: 2024, Номер unknown

Опубликована: Янв. 25, 2024

Halmahera Island is the result of interaction between Indo-Australian Plate, Molucca Sea and Philippine which gave rise to many active geological structures that are vulnerable seismic activity around island. The complexity this structure makes very interesting study. This study aims identify by integrating remote sensing gravity satellite data. Surface lineament analysis using method was carried out based on Sentinel-1A imagery uses GGMPlus data clarify continuity cannot be clearly mapped surface. Lineament techniques such as fast sigmoid edge detection (FSED) Euler deconvolution. results interpretation show NE-SW controls northern northeastern arms Halmahera. In contrast, southern arm dominated NW-SE structure. depth estimation shows area contributes having at various depths. Deep reach 4 km, while shallow found depths up 2 km. Earthquake hypocenter strengthen comprehensive yields an excellent correlation in describing general structural framework area. new finding trending arm, may caused two different tectonics first, subduction Plate with west. Second, strike-slip movement NE-SW, cuts due rotational thrust fault

Язык: 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, Год журнала: 2024, Номер 16(13), С. 2394 - 2394

Опубликована: Июнь 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

Язык: Английский

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Applications and Advancements of Spaceborne InSAR in Landslide Monitoring and Susceptibility Mapping: A Systematic Review

Remote Sensing, Год журнала: 2025, Номер 17(6), С. 999 - 999

Опубликована: Март 12, 2025

Landslides pose significant threats to human safety and socio-economic development. In recent decades, interferometric synthetic aperture radar (InSAR) technology has emerged as a powerful tool for investigating landslides. This study systematically reviews the applications of spaceborne InSAR in landslide monitoring susceptibility mapping over past decade. We highlight advancements key areas, including atmospheric delay correction, 3D monitoring, failure time prediction, enhancements spatial temporal resolution, integration with other technologies like Global Navigation Satellite System (GNSS) physical models. Additionally, we summarize various application strategies mapping, identifying gap between static nature most current studies InSAR’s dynamic potential capturing deformation velocity. Future research should integrate InSAR-derived factors variables rainfall soil moisture prediction. also emphasize that further development will require more efficient SAR data management processing strategies.

Язык: Английский

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Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model

Remote Sensing, Год журнала: 2023, Номер 15(22), С. 5427 - 5427

Опубликована: Ноя. 20, 2023

Complex and fragile geological conditions combined with periodic fluctuations in reservoir water levels have led to frequent landslide disasters the Three Gorges Reservoir area. With development of remote sensing technology, many scholars applied it susceptibility assessment improve model accuracy; however, how couple these two obtain optimal remains be studied. Based on Sentinel-1 data, relevant existing research results, information value method (IV), random forest (RF), support vector machine (SVM), convolutional neural network (CNN) models were selected analyze urban area Wanzhou. Models superior performance will coupled PS-InSAR deformation data using methods: joint training weighted overlay. The accuracy different was assessed compared aim determining coupling role InSAR model. results indicate that prediction is ranked as RF > SVM CNN IV. Among dynamic models, ranking follows: jointly trained (IJRF) overlay (IWRF) (IJSVM) (IWSVM). Notably, IJRF model, which combines through training, exhibited highest accuracy, an AUC 0.995. In factor importance analysis within third after hydrological distance (0.210) elevation (0.163), a 0.154. A comparison between mapping (LDSM) (LSM) revealed inclusion effectively reduced false positives around areas. suggest most suitable method, allowing for expression enhancing predictive This study serves reference future provides foundation risk management.

Язык: Английский

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Active thickness estimation and failure simulation of translational landslide using multi-orbit InSAR observations: A case study of the Xiongba landslide

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 129, С. 103801 - 103801

Опубликована: Март 29, 2024

The active thickness of the translational landslides plays a pivotal role in evaluating its hazards and simulating instability. Existing techniques have difficulties estimating accurate due to limitations observation conditions imaging geometry, leading deviations failure simulations. To overcome these challenges, this study proposes an enhanced method that utilizes multi-orbit Interferometric Synthetic Aperture Radar (InSAR) observations estimate subsequently conduct more instability involves integrating InSAR parameters with spatial geometry landslide establish slope coordinate system. This system enables projection one-dimensional Line Of Sight (LOS) displacements onto three-dimensional landslide. Subsequently, is estimated by combining mass conservation method. Finally, incorporated into geological model construction simulate dynamic movement was applied Xiongba Gongga County, Tibet Autonomous Region, China. results show deformation mainly concentrated at forefront, maximum rates 4.7 m/a, 2.3 1.24 m/a. encompasses area around 5.33 square kilometers, varies from 0 106.59 m. displacement distance reaches 1469.76 m, peak velocity 60.37 m/s. proposed provides scientific support for assessing, analyzing, preventing disasters.

Язык: Английский

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Integrated Assessment of Coastal Subsidence in Nansha District, Guangzhou City, China: Insights from SBAS-InSAR Monitoring and Risk Evaluation

Remote Sensing, Год журнала: 2024, Номер 16(2), С. 248 - 248

Опубликована: Янв. 8, 2024

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global soft-soil urban areas, faces significant challenges related ground subsidence. However, the current understanding of status, causative factors, risk (includes susceptibility vulnerability) assessment unclear. To address this gap, we utilized SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 June 2019, for systematic monitoring. Subsequently, assessed using a comprehensive index method matrix. Our findings indicate that velocity primarily ranged −40 −5 mm/a, spatial pattern increasing inland areas. The cumulative process unfolded four distinct stages. genesis land was linked an endogenous geological context dominated by deposition, influenced external factors such as surface loading groundwater extraction. High-risk zones were concentrated key engineering development transportation pipeline trunk lines, densely populated regions, demanding special attention. This study provides foundational resource prevention control strategies similar cities.

Язык: Английский

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