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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Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123094 - 123094

Published: Nov. 2, 2024

Language: Английский

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Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning

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

Published: Jan. 11, 2025

Abstract Floods are natural disasters with significant economic and infrastructural impacts. Assessing flood susceptibility in mountainous urban regions is particularly challenging due to the complicated interaction which structures terrain affect behavior. This study employs two ensemble machine learning algorithms, Extreme Gradient Boosting (XGBoost) Random Forest (RF), develop maps for Hunza-Nagar region, has been experiencing frequent flooding past three decades. An unsteady flow simulation carried out HEC-RAS utilizing a 100-year return period hydrograph as an input boundary condition, output of provided spatial inundation extents necessary developing inventory. Ten explanatory factors, including climatic, geological, geomorphological features namely elevation, slope, curvature, topographic wetness index (TWI), normalized difference vegetation (NDVI), land use cover (LULC), rainfall, lithology, distance roads rivers considered mapping. For inventory, random sampling technique adopted create repository non-flood points, incorporating ten geo-environmental conditioning factors. The models’ accuracy assessed using area under curve (AUC) receiver operating characteristics (ROC). prediction rate AUC values 0.912 RF 0.893 XGBoost, also demonstrating superior performance accuracy, precision, recall, F1-score, kappa evaluation metrics. Consequently, model selected represent map area. resulting will assist national disaster management infrastructure development authorities identifying high susceptible zones carrying early mitigation actions future floods.

Language: Английский

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One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(5), P. 4419 - 4440

Published: July 6, 2024

Abstract Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates efficacy Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on frequent tropical cyclone-induced Thanh Hoa province, North Central Vietnam. The 1D-CNN was structured four convolutional layers, two pooling one flattened layer, fully connected employing ADAM algorithm for optimization Mean Squared Error (MSE) loss calculation. A geodatabase containing 2540 flood locations 12 influencing factors compiled using multi-source geospatial data. database used to train check model. results indicate that model achieved high predictive accuracy (90.2%), along Kappa value 0.804 an AUC (Area Under Curve) 0.969, surpassing benchmark models such as SVM (Support Vector Machine) LR (Logistic Regression). concludes highly effective tool modeling floods.

Language: Английский

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Enhanced Root Zone Soil Moisture Monitoring Using Multitemporal Remote Sensing Data and Machine Learning Techniques

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 101354 - 101354

Published: Sept. 1, 2024

Language: Английский

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Urban flood management from the lens of social media data using machine learning algorithms

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132750 - 132750

Published: Jan. 1, 2025

Language: Английский

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Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 25, 2025

Language: Английский

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Improving index-based coastal vulnerability assessment using machine learning in Oman

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 976, P. 179311 - 179311

Published: April 9, 2025

Language: Английский

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Future flood susceptibility mapping under climate and land use change

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 11, 2025

Language: Английский

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Data Uncertainty of Flood Susceptibility Using Non-Flood Samples

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 375 - 375

Published: Jan. 23, 2025

Flood susceptibility provides scientific support for flood prevention planning and infrastructure development by identifying assessing flood-prone areas. The uncertainty posed non-flood sample datasets remains a key challenge in mapping. Therefore, this study proposes novel sampling method points. A model is constructed using machine learning algorithm to examine the due point selection. influencing factors of are analyzed through interpretable models. Compared generated random with buffer method, dataset spatial range identified frequency ratio one-class vector achieves higher accuracy. This significantly improves simulation accuracy model, an increase 24% ENSEMBLE model. (2) In constructing optimal dataset, demonstrates than other methods, AUC 0.95. (3) northern southeastern regions Zijiang River Basin have extremely high susceptibility. Elevation drainage density as causing these areas, whereas southwestern region exhibits low elevation. (4) Elevation, slope, three most important affecting Lower values elevation slope correlate offers new approach reducing technical disaster mitigation basin.

Language: Английский

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