Explainable Sinkhole Susceptibility Mapping Using Machine-Learning-Based SHAP: Quantifying and Comparing the Effects of Contributing Factors in Konya, Türkiye

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3139 - 3139

Опубликована: Март 13, 2025

Sinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employed for sinkhole susceptibility mapping (SSM). However, the lack of explainability inherent these methods remains critical issue decision-makers. this study, Konya Closed Basin was mapped using an interpretable model based on SHapley Additive exPlanations (SHAP). The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Machine (LightGBM) algorithms were employed, interpretability results enhanced through SHAP analysis. Among compared models, RF demonstrated highest performance, achieving accuracy 95.5% AUC score 98.8%, consequently selected development final map. analyses revealed that factors such as proximity to fault lines, mean annual precipitation, bicarbonate concentration difference are most variables influencing formation. Additionally, specific threshold values quantified, effects contributing analyzed detail. This study underscores importance employing eXplainable Artificial Intelligence (XAI) natural hazard modeling, SSM example, thereby providing decision-makers with more reliable comparable risk assessment.

Язык: Английский

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Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0318835 - e0318835

Опубликована: Март 28, 2025

Accurate forecasting of blowing snow events is vital for improving numerical models processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect using meteorological and flux monitoring data from three weather stations in the Alps. Through 5-fold cross-validation, model achieved impressive performance metrics, with precision rates exceeding 0.94 non-blowing 0.77-0.80 events. The SHAP framework was employed analyze relative importance factors, revealing that maximum wind speed (WS-MAX), average (WS-AVG), air temperature (AT), humidity (AH) are most influential factors. Additionally, Partial dependence plots (PDP) demonstrated a linear correlation between increased WS-MAX probability snow, while WS-AVG showed diminishing returns beyond 10 m/s. Notably, AT below -3°C strongly correlates occurrence, whereas above exhibits negative relationship. Relative plays significant role, values 60% stabilizing peaking near 100%. research contributes drifting event dynamics by integrating explainable artificial intelligence techniques (XAI), thereby interpretability supporting data-driven decision-making applications.

Язык: Английский

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Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection

Remote Sensing of Environment, Год журнала: 2023, Номер 301, С. 113901 - 113901

Опубликована: Дек. 2, 2023

Язык: Английский

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Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Advances in Engineering Software, Год журнала: 2024, Номер 192, С. 103648 - 103648

Опубликована: Апрель 11, 2024

Язык: Английский

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Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

The Science of The Total Environment, Год журнала: 2024, Номер 949, С. 175059 - 175059

Опубликована: Июль 29, 2024

Язык: Английский

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Evaluating the uncertainty in landslide susceptibility prediction: effect of spatial data variability and evaluation unit choices

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(3)

Опубликована: Фев. 28, 2025

Язык: Английский

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Testing snowpack layers by Raman spectroscopy

Science China Earth Sciences, Год журнала: 2025, Номер unknown

Опубликована: Апрель 11, 2025

Язык: Английский

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Comparison of two 2-D numerical models for snow avalanche simulation

The Science of The Total Environment, Год журнала: 2023, Номер 896, С. 165221 - 165221

Опубликована: Июнь 29, 2023

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena complex events, for this reason, different numerical models have been developed to reproduce their dynamics over given topography. In study, we focus on two-dimensional simulation tools RAMMS::AVALANCHE FLO-2D, aiming compare performance predicting deposition area avalanches. We also aim assess employment FLO-2D model, normally used water flood or mud/debris flow simulations, motion For purpose, two well-documented avalanche events that occurred Province Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The each case study was simulated with both through back-analysis processes. results evaluated primarily comparing observed one statistical indices. Subsequently, maximum depth, velocity depth compared between results. showed generally reproduced deposits better simulation. provided suitable wet dry after meticulous calibration rheological parameters, since they not those typically considered rheology studies. can be propagation could adopted practitioners define hazard areas, expanding its field application.

Язык: Английский

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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Atmosphere, Год журнала: 2024, Номер 15(11), С. 1343 - 1343

Опубликована: Ноя. 9, 2024

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts snowfall temperature patterns, it is increasingly important improve our ability monitor predict avalanches. This review explores use remote sensing technologies understanding key geomorphological, geobotanical, meteorological factors that contribute avalanche formation. The primary objective assess how can enhance risk assessment monitoring systems. A systematic literature was conducted, focusing on studies published between 2010 2025. analysis involved screening relevant sensing, dynamics, data processing techniques. Key sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, Landsat-8, combined with machine learning, fusion, detection algorithms process interpret data. found significantly improves by providing continuous, large-scale coverage snowpack stability terrain features. Optical radar imagery enable crucial parameters like snow cover, slope, vegetation influence risks. However, challenges limitations spatial temporal resolution real-time were identified. Emerging technologies, including microsatellites hyperspectral imaging, offer potential solutions these issues. practical implications findings underscore importance integrating ground-based observations for more robust forecasting. Enhanced fusion techniques will disaster management, allowing quicker response times effective policymaking mitigate avalanche-prone regions.

Язык: Английский

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Coupling Different Machine Learning and Meta-Heuristic Optimization Techniques to Generate the Snow Avalanche Susceptibility Map in the French Alps

Water, Год журнала: 2024, Номер 16(22), С. 3247 - 3247

Опубликована: Ноя. 12, 2024

The focus of this study is to introduce a hybrid predictive framework encompassing different meta-heuristic optimization and machine learning techniques identify the regions susceptible snow avalanches. To accomplish aim, present research sought acquire best-performed model among nine scenarios three meta-heuristics, namely particle swarm (PSO), gravitational search algorithm (GSA), Cuckoo Search (CS), ML approaches, i.e., support vector classification (SVC), stochastic gradient boosting (SGB), k-nearest neighbors (KNN), pertaining families. According diligent analysis performed with regard blinded testing set, PSO-SGB illustrated most satisfactory performance an accuracy 0.815, while precision recall were found be 0.824 0.821, respectively. F1-score predictions was area under receiver operating curve (AUC) obtained 0.9. Despite attaining similar success via CS-SGB model, time-efficiency underscored PSO-SGB, as corresponding process consumed considerably less computational time compared its counterpart. SHapley Additive exPlanations (SHAP) implementation further informed that slope, elevation, wind speed are contributing attributes detecting avalanche susceptibility in French Alps.

Язык: Английский

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