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- MakaleYalnız künye2026Scopus SJRQ1Very fast Kolmogorov-Arnold network for landslide susceptibility prediction
Günen, Mehmet Akif; Tona, Aziz Uğur
Landslides represent critical geological events causing loss of life and property. Prediction of landslide susceptibility is essential for risk reduction and decision-making. XGBoost, LightGBM, NGBoost, CatBoost, MLP, and FastKAN were used to predict landslide susceptibility in Gümüşhane, Türkiye, a region with complex topographic and geological structures. 20 factors were used: 9 topographical, 3 hydrological, 4 climatic, 2 geological, and 2 environmental and infrastructure-related factors. Model performance was assessed using Accuracy, Precision, Recall, and F-Score, with SHAP analysis showing feature contributions. A multicollinearity analysis was used to determine the final feature set. Topographical variables (Aspect, Slope) showed lower impacts, while geological and environmental factors (Lithology, Distance to Fault) demonstrated moderate influence across models. For FastKAN, DEM, precipitation, and distance to fault were key factors, while for boosting methods, GHI, Tmax, and DEM were most significant. FastKAN achieved the highest performance under the internal pixel-wise evaluation scheme, with an accuracy of 96.54%, followed by XGBoost at 95.56%. McNemar’s statistical test showed no significant difference between NGBoost and MLP. Post-inventory landslide events observed in 2025 and 2026 were spatially consistent with high and very high susceptibility zones, providing a qualitative consistency check rather than rigorous independent quantitative validation.
- MakaleYalnız künye2026Scopus SJRQ3Burned area detection using Sentinel-1 and Sentinel-2 features and analysis of ensemble models with explainable AI
Günen, Mehmet Akif; Türk, Salih
Wildfires are serious environmental disasters that threaten ecosystem sustainability, reduce biodiversity, and disrupt the global carbon cycle. This study aimed to improve the accuracy and interpretability of burned area mapping in the Bodrum, Köyceğiz, and Marmaris districts of Muğla province, Türkiye, by integrating multi-sensor satellite data with ensemble machine learning and explainable artificial intelligence (XAI). Sentinel-1 and Sentinel-2 imagery were processed to derive 50 optical and radar-based features, which were classified using four ensemble methods: AdaBoost.M1, Random Forest, GentleBoost, and RUSBoost. The SHapley Additive exPlanations framework was applied to quantify the contribution of each feature and guide feature selection. Results indicated that Sentinel-2 spectral indices, particularly NBR, NBRSWIR, and bands B12 and B8, were the most decisive variables for burned area classification, while Sentinel-1 features contributed less strongly overall but the IVH_ratio variable emerged as the most informative radar-based indicator. Among the classifiers, AdaBoost.M1 achieved the most stable and accurate performance, Random Forest showed strong sensitivity to spectral variability, GentleBoost performed effectively with reduced feature sets, and RUSBoost addressed class imbalance but showed weaker spatial continuity. Importantly, classification accuracy saturated after approximately 15 features, demonstrating that high accuracy can be achieved with fewer inputs and lower computational demand. Moreover, McNemar’s statistical test confirmed that the observed differences among classifiers were significant, highlighting GentleBoost’s clear superiority over other methods. Overall, the integration of optical and radar data with XAI provides a transparent, efficient, and replicable framework for operational burned area monitoring in Mediterranean ecosystems.
AtıfScopus2WoS3OpenAlex2İncele - MakaleYayıncıda açık erişim2026Scopus SJRQ4A data-driven decision-support framework for prioritizing retention interventions among high attrition-risk employee segments
Şimşek, Tuğçe; Şimşek, Aybala
Predicting employee attrition risk provides a useful signal for human resource decision-making. However, this signal alone does not determine which employee groups should be targeted first for retention interventions. This study develops a data-driven decision-support framework for prioritizing retention interventions among employees at risk of attrition. The framework uses the IBM HR Analytics Employee Attrition and Performance dataset and integrates calibrated machine learning, SHAP-based explanatory analysis, Entropy-CRITIC weighting, and PROMETHEE II ranking. Machine-learning models were first used to estimate employee-level attrition probabilities. These probabilities were then aggregated into segments defined by combinations of Department, JobRole, and OverTime, from which the high-risk segment pool was identified for multi-criteria analysis. Eight high-risk segments were detected. The segment ranked first by PROMETHEE II was not the segment with the highest attrition risk, indicating that intervention priority and attrition risk represent distinct decision outputs. Sensitivity analyses further showed that the segment-ranking structure remained stable under alternative weight-shift and threshold scenarios. The proposed framework is therefore not intended to identify individual employees at risk; rather, it supports transparent, auditable, and ethically compliant segment-level retention decisions.