4) Mr. Seifu Woldemichael Busho

Identifying optimal sediment sampling sites is crucial for accurate geochronological analysis and paleoclimatic reconstruction in arid regions. Nevertheless, systematic site suitability mapping in extensive desert environments remains limited. This study introduces a novel large-scale hybrid modeling framework that integrates multi-criteria decision-making (AHP and Entropy-TOPSIS) and data driven ensemble machine learning models (random forest and extreme gradient boosting) to determine the suitability of sediment sampling sites for optically stimulated luminescence dating in the Karakum and Kyzylkum Deserts. Nine key parameters, including environmental, climatic, and geomorphic contexts, were employed to develop the suitability layers. Additionally, accessibility factors were considered for practical field sampling. The findings revealed the suitability categorizes the landscape into very low (27.4%), low (18.9%), moderate (23.0%), high (8.7%), and very high (21.9%). High-suitability zones were spatially concentrated in the northern and central Karakum, as well as in the southwestern Kyzylkum regions, and the SHAP results revealed that mineral quartz and thermal exposure significantly influenced suitability predictions. This study provides the first high-resolution sediment sampling suitability model for Central Asia, and the integrated approach exhibits high predictive performance and spatial credibility, presenting a transferable framework for prioritizing sites within comparable geomorphic settings. The findings serve as a foundation for targeted field campaigns, thereby enhances the efficiency and accuracy of sediment-based paleoenvironmental research in desert environments, while also contributing to broader regional climate resilience efforts.