Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
PloS one
Tracing the pathways to Yathrib (Al-Madinah): A GIS-based analysis of settlement patterns and ancient trade networks
Ancient human activities in the arid Arabia peninsula are considered one of interesting research subjects in the landscape archaeological field to understand the relationships between human occupation strategies, material culture, and the natural environment in the past. Focusing on Al-Madinah Region of the Arabian Peninsula, a landscape of profound historical and archaeological significance, the research integrates Multi-Criteria Spatial Analysis (MCSA) using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) with Least-Cost Path (LCP) modeling. The study aimed to (i) evaluate environmental suitability for historical settlements and (ii) reconstruct optimal caravan routes converging on Yathrib (Al-Madinah). Six principal environmental and topographic variables were evaluated: lithology, elevation, slope, drainage density, distance to watercourses (wadis), and the soil classification. A balanced weighting system was implemented (Consistency Ratio = 0.01), where proximity to watercourses, slope, and soil were identified as the primary controlling factors. The resulting settlement suitability map identified 32.01% of the study area as highly suitable. For the reconstruction of caravan routes, a cost surface was generated based on four parameters: distance to watercourses, slope, elevation, and lithology to simulate LCPs to major archaeological sites, including Khaybar, Al-Hinakiyah, Dadan-AlUla-Hegra, and the coastal port of Al-Jar. Quantitative validation of these modeled routes against verified historical trade stations demonstrates that hydrological accessibility and terrain favorability jointly governed patterns of ancient human settlement and mobility, offering a replicable methodology for exploring trade networks and habitation dynamics in arid landscapes.
12 May 2026
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Optimizing LSTM networks and feature selection algorithms using GEE data
Feature selection uncertainty and suboptimal model configuration in flood susceptibility mapping (FSM) are critical challenges for disaster risk reduction. This study introduces a novel integrated framework that couples feature selection strategy with metaheuristic-optimized deep learning for high-precision FSM in the flood-prone Khuzestan Province, Iran. An initial set of 19 factors was sourced from Google Earth Engine (GEE). The most influential variables were identified using an ensemble of nine feature selection methods, including Boruta, Boruta-SHAP, Elastic-Net, Mutual Information, Permutation Importance, Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Stability Selection, and Deep Feature Importance. For model development, 1,000 sample points were used, consisting of 500 randomly selected non-flood points (value 0) and 500 flood points (value 1), with the trained model subsequently generalized to the entire study area. In this process, a frequency-based consensus rule was applied, whereby variables were retained only if selected by a majority of methods. This process established the Normalized Difference Vegetation Index (NDVI) and Daily Minimum Temperature (TMMN) as the most critical predictors. A Long Short-Term Memory (LSTM) was developed using this optimal feature set and subsequently enhanced through hyperparameter optimization with five advanced metaheuristic algorithms, including WOA, GWO, OOA, CSA, and HOA. The model validation demonstrated that optimization significantly boosted performance, with the LSTM-WOA model emerging as superior, achieving the highest F1-Score (0.88) and Cohen's Kappa (0.75). The final FSM identified the northwestern and central regions as the highest susceptibility. The study innovation lies in its formalized consensus feature selection and comparative metaheuristic optimization, providing a reliable tool for FSM in arid and semi-arid regions.
3 May 2026
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