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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Comparing the effectiveness of landslide susceptibility mapping by using the Frequency ratio and hybrid MCDM models

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103205 - 103205

Опубликована: Окт. 1, 2024

Язык: Английский

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Investigating the efficacy of physics-based metaheuristic algorithms in combination with explainable ensemble machine-learning models for landslide susceptibility mapping

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Язык: Английский

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Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 18, 2024

Язык: Английский

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Innovación educativa con sistemas de aprendizaje adaptativo impulsados por Inteligencia Artificial

Revista Internacional de Pedagogía e Innovación Educativa, Год журнала: 2024, Номер 4(2), С. 343 - 363

Опубликована: Июль 1, 2024

The emergence of artificial intelligence (AI) is transforming education through adaptive learning systems. These systems, based on AI algorithms, personalize the educational experience by adjusting to needs and styles each student. Using techniques such as machine deep learning, they analyze large volumes data generate personalized itineraries, breaking with homogeneous teaching model. Their implementation requires a suitable technological platform, solid infrastructure training teachers in use these tools. benefits are multiple: students receive real-time feedback progress at their own pace, improving motivation effectiveness, while can focus efforts higher value-added tasks obtain valuable information students' progress, facilitating teaching.

Язык: Английский

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Exploring uncertainty analysis in GIS-based Landslide susceptibility mapping models using machine learning in the Darjeeling Himalayas

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 14, 2024

Язык: Английский

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