A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

---

Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Engineering Geology, Год журнала: 2024, Номер 341, С. 107690 - 107690

Опубликована: Авг. 22, 2024

Язык: Английский

---

Research Trends and ‘Space-Sky-Ground-Underground’ Monitoring Technology Analysis of Landslide Hazard

Water, Год журнала: 2024, Номер 16(14), С. 2005 - 2005

Опубликована: Июль 15, 2024

Landslide is a typical geological disaster distributed in most countries worldwide. Due to long-term natural weathering and human engineering disturbances, the instability of landslides prone occur. Once monitoring disposal methods are implemented inappropriately, they can lead landslide hazards, seriously threatening safety people’s lives property. For long time, extensive research on has been conducted from various countries, providing crucial technical support for reducing incidence severity hazards. However, considering complex conditions actual direct impact internal external factors such as rainfall, storms, earthquakes, early warning accuracy hazards still relatively low. Therefore, based advanced achievements, it significant carry out current status development trends technology. Based Web Science core database, this study quantitatively analyzes achievements global past decade using bibliometric analysis. A systematic analysis technology according each study’s publication keywords, countries. On basis, multi-dimensional system was proposed, which utilizes complementary advantages achieve all-round, high-precision, real-time landslides. Finally, taking Xinpu Three Gorges Region China an example, multi-source multi-field-monitoring experiment conducted. The application provides essential reference monitoring, warning, well scientific prevention control hazard.

Язык: Английский

---

Image analysis and LSTM methods for forecasting surficial displacements of a landslide triggered by snowfall and rainfall

Landslides, Год журнала: 2024, Номер unknown

Опубликована: Авг. 16, 2024

Abstract Landslide-prone areas, predominantly located in mountainous regions with abundant rainfall, present unique challenges when subject to significant snowfall at high altitudes. Understanding the role of snow accumulation and melting, alongside rainfall other environmental variables like temperature humidity, is crucial for assessing landslide stability. To pursue this aim, study focuses first on quantification accumulated a slope through simple parameter obtained image processing. Then, included displacement prediction analysis carried out long short-term memory (LSTM) neural network. By employing processing algorithms filtering noise from white-shown rocks, methodology evaluates percentage cover RGB images. Subsequent LSTM forecasts utilize 28-day historical data snow, movements. The presented procedure applied case deep-seated Italy, site that winter 2020–2021 experienced heavy snowfall, leading slope. These episodes motivated aimed forecasting superficial displacements landslide, considering presence both time following days, along humidity temperature. This approach indirectly incorporates potential melting phenomena into model. Although subsequent winters were characterized by reduced including information model period demonstrated dependency predictions parameter, thus suggesting indeed factor accelerating In context, detecting incorporating it predictive emerges as aspect effects snowfall. method aims propose an innovative strategy can be future analyzed paper during upcoming well studies landslides altitudes lack precise precipitation recording instruments.

Язык: Английский

---

Unravelling long-term spatiotemporal deformation and hydrological triggers of slow-moving reservoir landslides with multi-platform SAR data

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 135, С. 104301 - 104301

Опубликована: Дек. 1, 2024

Язык: Английский

---

Enhancing 4‐D Landslide Monitoring and Block Interaction Analysis With a Novel Kalman‐Filter‐Based InSAR Approach

Journal of Geophysical Research Earth Surface, Год журнала: 2024, Номер 129(11)

Опубликована: Окт. 31, 2024

Abstract In recent years, Synthetic Aperture Radar Interferometry (InSAR) has become widely utilized for slow‐moving landslide monitoring due to its high resolution, accuracy, and extensive coverage. By integrating data from various orbits/platforms sources, one‐dimensional (1‐D) line‐of‐sight (LOS) InSAR measurements can be explored infer three‐dimensional (3‐D) movements. However, inconsistencies in observation times among different orbits sources pose challenges accurately capturing dynamic 3‐D movements over time (referred as 4‐D). this study, we propose a novel method, termed KFI‐4D that incorporates spatiotemporal constraints into the traditional Kalman filter. This enhancement transforms underdetermined problem of 4‐D movement acquisition parameter estimation problem, enabling precise The method was evaluated using both synthetic sets real Hooskanaden landslide, demonstrating an improvement exceeding 50% root mean square errors (RMSEs) compared conventional methods. Additionally, high‐resolution characteristics InSAR‐derived allow analysis strain invariants, providing insights block interactions dynamics. Our findings reveal invariants effectively indicate distribution activity blocks slip surfaces well their response triggers. Notably, abnormal signals identified prior catastrophic event at suggest potential early warning landslides. future integration advanced satellites, such NISAR, ALOS4 PALSAR3, Sentinel‐1C, is expected further enhance method's capabilities, improving temporal resolution monitoring.

Язык: Английский

---

Pre-, Co-, and Post-Failure Deformation Analysis of the Catastrophic Xinjing Open-Pit Coal Mine Landslide, China, from Optical and Radar Remote Sensing Observations

Remote Sensing, Год журнала: 2024, Номер 17(1), С. 19 - 19

Опубликована: Дек. 25, 2024

Landslide risks in open-pit mine areas are heightened by artificial slope modifications necessary for mining operations, endangering human life and property. On 22 February 2023, a catastrophic landslide occurred at the Xinjing Open-Pit Coal Mine Inner Mongolia, China, resulting 53 fatalities economic losses totaling 28.7 million USD. Investigating pre-, co-, post-failure deformation processes exploring potential driving mechanisms crucial to preventing similar tragedies. In this study, we used multi-source optical radar images alongside satellite geodetic methods analyze event. The results revealed pre-failure acceleration toe, large-scale southward displacement during collapse, ongoing across area due operations waste accumulation. collapse was primarily triggered an excessively steep, non-compliant design continuous excavation slope’s base. Furthermore, our experiments indicated that commonly Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) significantly underestimated maximum detectable gradient (MDDG) limitation. contrast, high-spatial-resolution Fucheng-1 provided more accurate monitoring with higher MDDG. This underscores importance of carefully assessing MDDG when employing InSAR techniques monitor rapid areas.

Язык: Английский

---

Advancing predictive accuracy of shallow landslide using strategic data augmentation

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

---

Development and in situ application of actively heated fiber Bragg grating cable for soil water content measurement

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

---

The Evaluation of Rainfall Forecasting in a Global Navigation Satellite System-Assisted Numerical Weather Prediction Model

Atmosphere, Год журнала: 2024, Номер 15(8), С. 992 - 992

Опубликована: Авг. 17, 2024

Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric data obtained from a global navigation satellite system (GNSS) into models to enhance accuracy and reliability rainfall forecasts. However, research on evaluating forecast different levels development corresponding forecasting platforms lacking. This study develops establishes platform supported by GNSS-assisted (WRF) model, quantitatively assessing effect GNSS precipitable (PWV) WRF model forecasts light rain (LR), moderate (MR), heavy (HR), torrential (TR). Three schemes are designed tested using seven ground meteorological stations in Xi’an City, China, 2021. The results show that assimilating PWV significantly improves levels, with root mean square error (RMSE) improvement rates 8%, 15%, 19%, 25% LR, MR, HR, TR, respectively. Additionally, RMSE demonstrates decreasing trend increasing magnitudes assimilated PWV, particularly effective range [50, 55) mm where lowest 3.58 mm. Moreover, shows improvements statistical indexes such as probability detection (POD), false alarm rate (FAR), threat score (TS), equitable (ETS) across all intensities, notable HR TR. These confirm high precision, visualization capabilities, robustness developed platform.

Язык: Английский

---