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- ArticleAçık Erişim2026Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Zerouali, Bilel; Alqadhi, Saeed; Katipoğlu, Okan Mert; Prasad, Pankaj; Arman, Hasan; Albanai, Jasem A; Mallick, Javed
Landslides significantly threaten natural and built environments, necessitating accurate prediction models for effective hazard mitigation. There is an urgent need to further improve the performance of machine learning algorithms in predicting landslide susceptibility by monitoring the impact of optimization algorithms on the performance of these models. This study evaluates the performance of various machine learning classifiers, including k-Nearest Neighbors (kNN), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), for landslide susceptibility mapping. Additionally, Particle Swarm Optimization (PSO) is employed to enhance model performance by optimizing hyperparameters. Mountainous areas in the eastern Mediterranean (the northern Kabir River basin in western Syria) were identified as a result of the high frequency of landslide events over the past two decades. Nineteen factors causing landslides were identified, with no factor excluded, as a result of a multicollinearity test. The results indicate that XGBoost achieves the highest performance among traditional models. When integrated with PSO, the PSO-XGBoost model further improves classification performance, demonstrating its robustness in handling complex spatial patterns. Feature importance analysis using SHAP confirms slope as the dominant factor, followed by TRI, rainfall, Aspect, TWI, and curvature, highlighting the role of topography and hydrology in landslide occurrence. Moderate lithology, NDVI, and LULC contributions and lower importance of Flow Accumulation and Soil Depth suggest complex environmental interactions. Model predictions show varying susceptibility distributions. PSO-MLP assigns the highest very high susceptibility (44.09%), while PSO-XGBoost provides a balanced classification (31.13%). The PSO-XGBoost model demonstrates superior predictive capability, offering reliable landslide susceptibility maps for disaster risk management and land-use planning.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Advancing Forest Fire Susceptibility Mapping in the Eastern Mediterranean Using Multi-Algorithm Machine Learning
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Alqadhi, Saeed; Prasad, Pankaj; Katipoğlu, Okan Mert; Nguyen-Huy, Thong; Das, Jayanta; Almohamad, Hussein; Mallick, Javed
Forest fires are among the most severe natural hazards threatening ecological sustainability in the Eastern Mediterranean, with Syria being a prominent example where ongoing conflict has undermined forest management and monitoring systems. Despite numerous global studies on forest fire susceptibility, conflict-affected areas in the Eastern Mediterranean remain underrepresented in the scientific literature due to limited data and complex environmental and social conditions. In particular, few studies have systematically compared multiple machine learning algorithms for predicting forest fire susceptibility in these regions. To address this gap, this study evaluates the performance of six machine learning algorithms—Extreme Gradient Boost (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), and Linear Regression (LR)—using 3,589 fire events and 13 conditioning factors in the Latakia governorate, western Syria. Results indicate excellent predictive performance for all models, with XGBoost achieving the highest AUC of 0.993, followed by RF (0.992), KNN (0.977), MLP (0.975), SVM (0.968), and LR (0.963). These findings provide a robust basis for supporting fire management and mitigation strategies in Syria and offer insights applicable to other conflict-affected regions of the Eastern Mediterranean.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Machine learning-driven flood susceptibility mapping: comparing model performances and feature influences in a coastal watershed of the Eastern mediterranean
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Bindajam, Ahmed Ali; Zerouali, Bilel; Katipoğlu, Okan Mert; Prasad, Pankaj; Alharbi, Maged Muteb; Ramadan, Mona S.; Mallick, Javed; Ghribi, Mounir
Flood susceptibility mapping is crucial for effective disaster management and mitigation in regions prone to extreme weather events. Despite advances in the use of conventional models for flood mapping, coastal basins in the Eastern Mediterranean still lack studies that employ machine learning (ML) techniques to accurately estimate flood risk. Most research focuses on quantitative prediction without systematically elucidating the influencing environmental factors, limiting the effectiveness of the results in applied contexts. Hence, the need to develop machine learning-based explanatory models that not only increase prediction accuracy but also reveal the most important determinants of flooding in complex coastal environments. This study assesses flood susceptibility using three ML algorithms, including, Light Gradient Boosting Machine (LGBM), Artificial neural networks (ANN), and eXtreme Gradient Boosting (XGBoost), with feature importance analysis conducted using SHapley Additive exPlanations (SHAP). The XGBoost model demonstrated superior performance in identifying flood susceptibility areas, compared to ANN, and LGBM. Feature importance analysis revealed that Aspect, Distance from River, and Slope were the most influential features, with Aspect being the most dominant predictor of flood susceptibility. The methodological integration of XGBoost with SHAP not only achieved high performance, but also provided interpretable insights into environmental drivers of flood risk. This combination represents a significant advancement over prior approaches, as it bridges predictive power with explainability, thereby enhancing the reliability of flood susceptibility mapping. The findings contribute to improved flood risk management strategies and lay the foundation for future research on interpretable and robust ML models for flood prediction.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective
Abdo, Hazem Ghassan; Richi, Sahar Mohammed; Prasad, Pankaj; Katipoğlu, Okan Mert; Halder, Bijay; Niknam, Arman; Hang, Hoang Thi; Alharbi, Maged Muteb; Mallick, Javed
Assessing landslide susceptibility is essential in urban planning and risk management. In the context of the Eastern Mediterranean region, there is a continuing need to compare the performance of machine learning (ML) algorithms in predicting landslide susceptibility, which can improve landslide risk management measures. Therefore, this study aims to evaluate and compare the predictive capabilities of three ML models: Multilayer perceptron (MLP), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost) models, in evaluating the susceptibility of various types of landslides and to refine the combination of causal factors. An evaluation of 19 conditioning factors, including topographical, geological, and environmental variables, was conducted to assess their effects on landslide susceptibility in different models in a geographic information system (GIS) environment. The results show that "Elevation" and "Slope" were consistently identified as the most influential factors in all models, with MLP demonstrating the greatest sensitivity to "Elevation." The study area was divided into five susceptibility categories: very low, low, moderate, high, and very high. According to the LGBM model, 24.27% of the area was classified as "very low" susceptibility, while the XGBoost and MLP models identified 25.69% and 27.28%, respectively. On the other hand, the "very high" susceptibility category covered 19.57%, 20.31%, and 19.78% of the area for the LGBM, XGBoost, and MLP models, respectively. The AUC-ROC approach has been utilized to evaluate, validate, and compare the performance of different ML models. Our study found AUC values for three MLTs. These findings suggest that all models demonstrate reasonable accuracy in identifying susceptible zones, and XGBoost demonstrated the best performance among the MLTs, with an AUC of 92.6% compared to the others. The insights gained from this study can inform targeted mitigation strategies to reduce landslide risks in Lebanon.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Delineation of environmentally sustainable urban settlement using GIS-based MIF and AHP techniques
Liladhar Rane, Nitin; Purushottam Choudhary, Saurabh; Saha, Arjun; Srivastava, Aman; Pande, Chaitanya Baliram; Alshehri, Fahad; Roy, Ranjan; Katipoğlu, Okan Mert; Abdo, Hazem Ghassan
Choosing appropriate locations for built-up growth while maintaining natural balance has become necessary for sustainable urban planning. To this end, two multi-criteria decision-making (MCDM) approaches—the multi-influencing factor (MIF) and the analytical hierarchy process (AHP)—have been applied in this work. The MIF model results show that 23.546% of the study region was very highly suitable for newly built-up areas, and 18.786% was restricted for urban settlement, whereas, in the AHP model, 28.907% of the area was highly suitable, 17.619% was restricted for urban settlement. Finally, the dependability and efficiency of the two models used for the study area were assessed through the receiver operating curve (ROC) and area under curve (AUC) analysis. The comparative analysis demonstrates that the MIF approach (AUC 0.817) is more suitable and reliable than the AHP (AUC 0.783). The present work provides new insights to integrate GIS tools with MCDM for practical urban land suitability assessment.
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