Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 988 - 988

Published: March 12, 2024

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool mitigate such threats. In this regard, study considers the northern region of Pakistan, which is primarily susceptible landslides amid rugged topography, frequent seismic events, seasonal rainfall, carry out LSM. To achieve goal, pioneered fusion baseline models (logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, XGBoost). With a dataset comprising 228 landslide inventory maps, employed classifier correlation-based feature selection (CFS) approach identify twelve most parameters instigating landslides. The evaluated included slope angle, elevation, aspect, geological features, proximity faults, roads, streams, was revealed primary factor influencing distribution, followed by aspect rainfall minute margin. models, validated AUC 0.784, ACC 0.912, K 0.394 for logistic well 0.907, 0.927, 0.620 XGBoost, highlight practical effectiveness potency results superior performance LR among XGBoost ensembles, contributed development precise LSM area. may serve valuable guiding risk-mitigation strategies policies in geohazard-prone regions at national global scales.

Language: Английский

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Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches

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

Published: Oct. 1, 2024

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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Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas

Landslides, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Language: Английский

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Assessment of the effects of characterization methods selection on the landslide susceptibility: a comparison between logistic regression (LR), naive bayes (NB) and radial basis function network (RBF Network)

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(3)

Published: Feb. 15, 2025

Language: Английский

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Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский

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Advanced machine learning techniques for enhanced landslide susceptibility mapping: Integrating geotechnical parameters in the case of Southwestern Cyprus

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 3, 2025

Language: Английский

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Application of Naive Bayes, kernel logistic regression and alternation decision tree for landslide susceptibility mapping in Pengyang County, China

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(13), P. 12043 - 12079

Published: May 25, 2024

Language: Английский

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Increasing Landslide Susceptibility in Urbanized Areas of Petrópolis Identified Through Spatio-Temporal Analysis

Journal of South American Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 105509 - 105509

Published: April 1, 2025

Language: Английский

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Correlation Between Geotechnical Indexes and Landslide Occurrence in Southwestern Cyprus Using GIS and Machine Learning

Geotechnical and Geological Engineering, Journal Year: 2024, Volume and Issue: 43(1)

Published: Dec. 13, 2024

Abstract Landslides are significantly influenced by the properties of geological materials. As such, effective landslide susceptibility and hazard assessment necessitates use carefully selected well-organized spatial data on geology ground characteristics. The present study explores correlation between landslides geotechnical indexes pertinent to problem slope stability. For this purpose, a geodatabase containing was created for southwestern part island Cyprus, an area noted its frequent instability issues availability comprehensive database. Then, statistical correlations established recorded (active inactive) in region key geotechnical, geomorphological factors. analysis initially performed using Frequency Ratio method, followed two advanced machine learning techniques, namely Random Forest Shapley Additive Explanations. results reveal that weak argillaceous geomaterials, clay content plasticity index constitute high importance variables, factors such as angle. In rocky formations with clear rock mass structure, main emerge Geological Strength Index uniaxial compressive strength. strong identified distribution underscores potential benefit integrating these variables methodologies. This adjusted emphasis provides clearer insights into relationship occurrences, which is crucial developing more accurate predictive models mitigation strategies.

Language: Английский

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