Integrating GIS–Fuzzy logic framework and remotely sensed climate data for drought vulnerability assessment across Africa

Drought is a complex and severe hazard, particularly in Africa, where precipitation underpins livestock and agriculture—the core of the continent’s economy. This study maps drought vulnerability across Africa using a Geographic Information System (GIS)–based Fuzzy Logic Framework, incorporating temperature, precipitation, wind speed, water vapor pressure, and solar radiation factors. Semivariogram modeling was employed to understand the spatial variability of these climatic factors. Semivariogram modeling was chosen for its ability to quantify spatial dependencies, ensuring accurate variability assessment over large regions. Unlike deterministic methods, it captures continuous spatial autocorrelation, enhancing precision in drought vulnerability mapping. Fuzzy membership scores, scaled from 0 to 1, were allocated according to the relative contribution of each variable to drought vulnerability. The final output was classified into five categories: very severe, moderate, mild, slight, and no drought. An explanatory regression analysis was then performed to determine the optimal model for quantifying the influence of climatic variables on drought vulnerability. Model 30 emerged as the optimal fit, demonstrating an adjusted R² value of 0.9777 and the lowest Akaike's Information Criterion (AICc) value of 4428.887. Despite high accuracy, data resolution constraints and regional climate variability may introduce biases, limiting predictive adaptability across diverse ecosystems and future climate shifts. Additionally, the spatial autocorrelation tool Moran's I was utilized to verify the uniform distribution of standard residuals, ensuring the model's reliability. The study effectively presents the spatial extent of drought vulnerability across various African countries, highlighting the regions most at risk. This comprehensive analysis provides valuable insights into the geographic distribution of drought vulnerability, offering a robust framework for future research and practical applications in drought management and mitigation strategies. The results emphasize the significance of incorporating advanced GIS methodologies and fuzzy logic to improve the understanding of drought dynamics and to guide targeted measures aimed at reducing their impacts. Furthermore, the proposed model contributes to the development of early warning systems, precision drought interventions, and policy-driven resilience planning.