Landslide Risk Evaluation in Shenzhen Based on Stacking Ensemble Learning and InSAR

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2023, Номер 16, С. 1 - 18

Опубликована: Янв. 1, 2023

Construction activities of accelerated urbanization in Shenzhen have increased the landslide risk area, which has intensified potential threat to human and natural environment. However, landslides is poorly evaluated. In this paper, a evaluation (LRE) model constructed using susceptibility map (LSM) vulnerability. experiment, stacking ensemble learning (SEL) based on convolutional neural network (CNN), multilayer perceptron (MLP), gated recurrent unit (GRU) support vector machine regression (SVR) generate LSM by topography, geology, engineering activities, time-series precipitation normalized difference vegetation index (NDVI). Road network, building distribution density annual average data are used evaluate vulnerability entropy weight method. study, multiple statistical indicators performance model, Interferometric Synthetic Aperture Radar (InSAR) deformation utilized verify LRE results Shenzhen. The show that SEL method more refined for LSM, with best overall accuracy, especially receiver operating characteristic curve (ROC), where accuracy improved nearly 8%. Shenzhen, very high, moderate, low areas account 0.283%, 0.451%, 0.859%, 36.890% 61.517%, respectively. most high InSAR clear concentrated trend large rate. Research can provide technical disaster prevention

Язык: Английский

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Considering the effect of non-landslide sample selection on landslide susceptibility assessment

Geomatics Natural Hazards and Risk, Год журнала: 2024, Номер 15(1)

Опубликована: Авг. 21, 2024

Crafting landslide susceptibility mapping is pivotal for the effective management of risks. However, influence non-landslide sample selection on modeling performance assessment models remains a crucial challenge to overcome. This article employs Huize County as research area and identifies 12 factors that exert influence. In this study, we utilized Extreme Gradient Boosting Random Forest algorithms, four methods (Whole-area random method, Buffer Frequency Ratio Analysis Hierarchy Process) were employed select samples constructing model. The findings revealed model derived from different selections exhibited significant variations, obtained using buffer zone frequency ratio AHP method performed better than full-area Among evaluated models, demonstrated most optimal performance, with an AUC 92.17% XGBoost-AHP 91.64% RF-AHP. Based SHapley Additive explanation (SHAP), main variables impacting danger in elevation, NDVI, peak seismic acceleration. study provides theoretical support assessments interpretable AI research.

Язык: Английский

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Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas

Geocarto International, Год журнала: 2023, Номер 38(1)

Опубликована: Окт. 23, 2023

This study makes a significant contribution to the field of groundwater potential mapping (GWPM) by exploring application ensemble learning models (ELMs), specifically boosting (BEMs), which have not been fully utilized in GWPM. By employing six ELMs (random forest, AdaBoost, XGBoost, CatBoost, GBDT, and LightGBM), along with Tree-structured Parzen Estimator Luoning County, China, this identifies key indicators (topographic position index, distance rivers, topographic wetness index) demonstrates superior model performance XGBoost compared other ELMs. Additionally, correlation analysis confirms accuracy predicting relationships between important potentials. Finally, findings provide valuable insights for sustainable management strategies County emphasize need further exploration ELMs, development comprehensive evaluation indicator systems, reduction inconsistencies predication results practical research support future management.

Язык: Английский

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Addressing land use planning: A methodology for assessing pre- and post-landslide event urban configurations

The Science of The Total Environment, Год журнала: 2024, Номер 921, С. 171152 - 171152

Опубликована: Фев. 24, 2024

With urban areas projected to accommodate 68 % of the global population by 2050, imperative for inclusive, safe, and sustainable cities becomes paramount. In timeline centers, landslides represent one most destructive phenomena, involving several resources allocation with private public investments, sometimes claiming human lives. By synergically connecting environmental, planning, configurational spheres, this study seeks support proactive management landslide risk. The proposed three-step methodology allowed quantify environmental features involved in occurrence, evaluate planning framework vulnerabilities, suggest alternative configurations that experienced landslides. has been applied case a tragic Casamicciola Terme (Italy) November 2022. First, stream network drainage basin corresponding confluence point into sea have calculated (environmental elaborations). Subsequently, these elaborations overlapped runoff mitigation sediment deposition layers, extracted through INVEST software. Secondly, reconnaissance local superordinate levels realized, deepen tools cogency on area, contextually deepening constraints characterize it. From overlapping two steps, free risk located. Finally, based available territorial surface (Sta) cover ratio (Rct), configuration scenarios proposed, envisaging relocation buildings landslide. Results show originated three out five gullies. Some portions are still under high very hydrogeological Contextually, it emerges poor attention from planners framework. Historic settlement an Rct 33.64 %, while which relocate built up 32,45 scenario 1 27,9 2. resulted useful address supporting realization scenarios. We expect our research contribute evolving field disaster reduction, providing systematic approach manage

Язык: Английский

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Landslide Hazard Is Projected to Increase Across High Mountain Asia

Earth s Future, Год журнала: 2024, Номер 12(10)

Опубликована: Окт. 1, 2024

Abstract High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with nonlinear response relative increasing global temperatures. However, these changes are geographically varied. This article maps probable hazard, shown by indicator (LHI) derived from downscaled temperature. In order capture of slopes extreme precipitation, simple machine‐learning model was trained on database landslides across develop regional LHI. applied statistically data 30 members Seamless System Prediction Earth Research large ensembles produce range possible outcomes under Shared Socioeconomic Pathways 2‐4.5 5‐8.5. The LHI reveals will most parts Asia. Absolute increases be highest already hazardous areas such Central Himalaya, but change greatest Tibetan Plateau. Even regions where declines year 2100, it prior mid‐century mark. seasonal cycle occurrence not greatly Although substantial uncertainty remains projections, overall direction seems reliable. These findings highlight importance continued analysis inform disaster risk reduction strategies stakeholders

Язык: Английский

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Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning

Geoscience Frontiers, Год журнала: 2023, Номер 15(2), С. 101758 - 101758

Опубликована: Ноя. 22, 2023

With the development of landslide monitoring system, many attempts have been made to predict failure-time utilizing data displacements. Classical models (e.g., Verhulst, GM (1,1), and Saito models) that consider characteristics displacement determine investigated extensively. In practice, is continuously implemented with data-set updated, meaning predicted life expectancy (i.e., lag between time node at each instant conducting prediction) should be re-evaluated time. This manner termed "dynamic prediction". However, performances classical not discussed in context dynamic prediction yet. this study, such are firstly, disadvantages then reported, incorporating from four real landslides. Subsequently, a more qualified ensemble model proposed, where individual integrated by machine learning (ML)-based meta model. To evaluate quality under prediction, novel indicator 'discredit index (β)' higher value β indicates lower quality. It found Verhulst would produce results significantly β, while (1,1) indicate highest mean absolute error (MAE). Meanwhile, accurate than models. Here, performance decision tree regression (DTR)-based best among various ML-based

Язык: Английский

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Risk Assessment and Prevention Planning for Collapse Geological Hazards Considering Extreme Rainfall—A Case Study of Laoshan District in Eastern China

Land, Год журнала: 2023, Номер 12(8), С. 1558 - 1558

Опубликована: Авг. 6, 2023

Geological disasters refer to adverse geological phenomena that occur under the influence of natural or human factors and cause damage life property. Establishing prevention control zones based on disaster risk assessment results in land planning management is crucial for ensuring safe regional development. In recent years, there has been an increase extreme rainfall events, so it necessary conduct effective hazard assessments different conditions. Based first national survey results, this paper uses analytic hierarchy process (AHP) combined with information method (IM) construct four conditions, namely, 10-year, 20-year, 50-year, 100-year return periods. The susceptibility, hazard, vulnerability, Laoshan District eastern China are evaluated, established evaluation results. show that: (1) There 121 collapse District, generally at a low susceptibility level. (2) A positive correlation exists between hazards/risks. With condition changing from 10-year period period, proportion high-hazard increased 20% 41%, high-risk 31% 51%, respectively. Receiver operating characteristic (ROC) proved accuracy was acceptable. (3) Key, sub-key, general have established, corresponding suggestions proposed, providing reference early warning other regions.

Язык: Английский

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Risk assessment of disaster chain in multi-seam mining beneath gully topography

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 111, С. 104750 - 104750

Опубликована: Авг. 10, 2024

Язык: Английский

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Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios

Environmental Research Letters, Год журнала: 2024, Номер 19(12), С. 124016 - 124016

Опубликована: Окт. 23, 2024

Abstract Mountainous landslides are expected to worsen due environmental changes, yet few studies have quantified their future risks. To address this gap, we conducted a comprehensive analysis of the eastern Hindukush region Pakistan. A geospatial database was developed, and logistic regression employed evaluate baseline landslide susceptibility for 2020. Using latest coupled model intercomparison project 6 models under three shared socioeconomic pathways (SSPs) cellular automata-Markov model, projected rainfall land use/land cover patterns 2040, 2070, 2100, respectively. Our results reveal significant changes in use patterns, particularly long-term (2070 2100). Future then predicted based on these projections. By high-risk areas increase substantially all SSP scenarios, with largest increases observed SSP5-8.5 (56.52%), SSP2-4.5 (53.55%), SSP1-2.6 (22.45%). will rise by 43.08% (SSP1-2.6), 40.88% (SSP2-4.5), 12.60% (SSP5-8.5). However, minimal compared baseline, 9.45% 1.69% 7.63% These findings provide crucial insights into relationship between risks support development climate risk mitigation, planning, disaster management strategies mountainous regions.

Язык: Английский

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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China

Ecological Indicators, Год журнала: 2025, Номер 175, С. 113573 - 113573

Опубликована: Май 7, 2025

Язык: Английский

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