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

Geoscience Frontiers, Journal Year: 2024, Volume and Issue: 15(6), P. 101890 - 101890

Published: July 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.

Language: Английский

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Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 172 - 172

Published: Jan. 15, 2025

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method identifying key regional factors remains a challenging task. To address this, this study assessed performance six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF CNN+CatBoost), Stacking Ensemble (SE) combining CNN, RF, CatBoost in along Karakoram Highway northern Pakistan. Twelve were examined, categorized into Topography/Geomorphology, Land Cover/Vegetation, Geology, Hydrology, Anthropogenic Influence. A detailed inventory 272 occurrences was compiled to train models. proposed stacking ensemble improve modeling, with achieving an AUC 0.91. Hybrid modeling enhances accuracy, CNN–RF boosting RF’s from 0.85 0.89 CNN–CatBoost increasing CatBoost’s 0.87 0.90. Chi-square (χ2) values (9.8–21.2) p-values (<0.005) confirm statistical significance across This identifies approximately 20.70% area as high very risk, SE model excelling detecting high-risk zones. Key influencing showed slight variations while multicollinearity among variables remained minimal. approach reduces uncertainties, prediction supports decision-makers implementing effective mitigation strategies.

Language: Английский

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

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 999 - 999

Published: March 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.

Language: Английский

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Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

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Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

The Egyptian Journal of Remote Sensing and Space Science, Journal Year: 2024, Volume and Issue: 27(2), P. 255 - 267

Published: March 25, 2024

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed quasi-permanent scatterer method detect areas with and without in period from 2018 2020. In addition, 12 factors affecting were selected LS-prone areas. Information gain ratio (IGR) frequency methods used determine importance weighting of various sub-factors subsidence. Novel approaches, including standard adaptive-network-based fuzzy inference system (ANFIS) algorithm its integration particle swarm optimization (PSO) algorithm, yielded LS maps. The models' predictive performance was assessed using statistical indexes such as root mean square error (RMSE), area under receiver operating characteristic curve (AUROC) confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, recall). Finally, Shapley additive explanations approach explore features modeling. findings showed that pattern V-shaped, averaging 321 mm/year. Ground-leveling interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, decline water table, thickness played pivotal roles occurrences. ANFIS-PSO model classified approximately 50.26 % high very susceptibility. AUROC values ANFIS models for training dataset 0.912 0.807, respectively. For testing dataset, produced higher value 0.863, while had 0.771. RMSE lower. Given remarkable deemed suitable evaluating susceptibility area.

Language: Английский

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Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region

Land, Journal Year: 2024, Volume and Issue: 13(7), P. 1011 - 1011

Published: July 8, 2024

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets determining the most effective methods pose significant challenges. This study assesses performance seven machine learning classifiers Himalayan region China–Pakistan Economic Corridor, utilizing statistical techniques validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological geological (2), meteorological (1) factors. These evaluated for multicollinearity, feature importance, their influence incidences. Our findings indicate that Support Vector Machines Logistic Regression highly effective, particularly near fault zones roads, due to effectiveness handling complex, non-linear terrain interactions. Conversely, Random Forest demonstrated variability results. Each model distinctly identified ranging from very low high risk. Significant conditioning such as elevation, rainfall, lithology, slope, use identified, reflecting unique geomorphological conditions Himalayas. Further analysis using Variance Inflation Factor Pearson correlation coefficient showed minimal multicollinearity among variables. Moreover, evaluations Area Under Receiver Operating Characteristic Curve (AUC-ROC) values confirmed strong predictive capabilities models, with Classifier performing exceptionally well, achieving an AUC 0.96 F-Score 0.86. shows importance selection based dataset characteristics enhance decision-making strategy effectiveness.

Language: Английский

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Geomorphological and Geological Characteristics Slope Unit: Advancing Township-Scale Landslide Susceptibility Assessment Strategies

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 355 - 355

Published: Feb. 9, 2025

The current method for dividing slope units primarily relies on hydrological analysis methods, which consider only geomorphological factors and fail to reveal the geological boundaries during landslides. Consequently, this approach does not fully satisfy requirements detailed landslide susceptibility assessments at township scale. To address limitation, we propose a new evaluation model based characteristics. key challenges addressed include: (i) Optimization of unit division method. This is accomplished by integrating features, such as gradient aspect, with including lithology, structure types, disaster categories, develop process extracting both results indicate that proposed outperform methods in three indicators: overlap, shape regularity, spatial distribution uniformity. (ii) Development validation model. A index system developed using multi-source data, prediction conducted via XGBoost optimized Bayesian methods. model’s accuracy validated Receiver Operating Characteristic (ROC) curve. show achieve an AUC value 0.973, surpassing (iii) Analysis variations. two types analyzed through case studies. consistency between field verification explained engineering SHAP then used examine influence disaster-inducing individual occurrence.

Language: Английский

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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 577 - 577

Published: March 10, 2025

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims predict the spatial distribution of potential geological using machine learning models ArcGIS-based analysis. A dataset comprising 2700 known hazard locations Yibin City was analyzed extract key environmental topographic features influencing susceptibility. Several were evaluated, including random forest, XGBoost, CatBoost, with model optimization performed Sparrow Search Algorithm (SSA) enhance prediction accuracy. produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct pattern characterized by concentration mountainous areas such as Pingshan County, Junlian Gong while plains exhibited relatively lower risk. Among different types, landslides found be most prevalent. The results further indicate strong overlap between predicted zones existing rural settlements, highlighting challenges resilience these areas. research provides refined methodological framework for integrating geospatial analysis prediction. findings offer valuable insights land use planning mitigation strategies, emphasizing necessity adopting “small aggregations multi-point placement” approach settlement Sichuan’s regions.

Language: Английский

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Apparent dip sliding mechanism of obliquely inclined bedding rockslides: A case study of the Shanyang landslide, China

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(5)

Published: April 7, 2025

Language: Английский

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