Optimizing Humanitarian Logistic: Securing Aid Corridors in Afghanistan Using Predictive Analytics

Abstract

Operating in conflict zones, humanitarian organizations are exposed to insecurity, broken supply chains, and unpredictable political  environments. These complexities are seen in Afghanistan, which needs new, data-driven logistics approaches. This study  proposes a predictive analytics framework based on ensemble machine learning models, Bagging, Boosting, and  Stacking to predict fatalities, demonstration events, and high-risk areas to support humanitarian logistics, supply chain  optimization, and corridor management in Afghanistan. As a method, it applies conflict data to geospatial and  temporal variables to predict security risks well. Predictive outcomes revealed peak risk times and places, which were  useful in making logical decisions, optimizing resources, and increasing the safety of humanitarian corridors. The framework’s  reliability was validated using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error  (RMSE), and classification accuracy. This research is significant because it applies predictive analytics theory to real  logistical scenarios with a scalable model that can be used by other humanitarian organizations. The main limitations of the  study include the small area of analysis, the absence of socio-economic variables in the analysis, and the  lack of integration of real-time data. Future work should include the integration of real-time data streams,  extension of the geographical coverage, and the integration of dynamic socio-economic and political variables to improve the accuracy  of the prediction model. This study effectively enables peak risk periods and locations to be pinpointed, which  in turn enables better targeted interventions to improve the safety of humanitarian workers and reach vulnerable populations on  time.