Financial Risk and Customs Control in Humanitarian Water Logistics: A Machine Learning Approach.
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Дата
2026-02-07
Назва журналу
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Назва тому
Видавець
CEUR
Анотація
This study explores the application of machine learning to mitigate financial and regulatory risks in humanitarian water logistics. Through the WaterWayfinder mobile platform, aid coordinators in Ukraine’s Kherson and Zaporizhzhia regions achieved measurable gains in operational efficiency and compliance. AI-driven route optimization reduced delivery times by up to 32% and fuel costs by 22%, while predictive modeling improved resource allocation and reduced exposure to high-cost disruptions. The system’s customs control module enabled pre-clearance planning and real-time regulatory updates, shortening border processing times by an average of 2.5 hours per shipment. Despite connectivity and data challenges, WaterWayfinder demonstrated resilience and adaptability in conflict-affected environments. Its modular architecture, offline capabilities, and integration with geospatial intelligence
position it for broader deployment across crisis zones. The findings highlight WaterWayfinder’s potential as a scalable, data-driven framework for intelligent humanitarian logistics, aligning with global efforts to enhance transparency, agility, and cross-border coordination in aid delivery.
Опис
Ключові слова
machine learning, financial risk, customs control, WaterWayfinder, GIS, mobile application
Бібліографічний опис
Dumanska I., Pavlova O., Rabcan J., Melnyk A., Kharun O. Financial Risk and Customs Control in Humanitarian Water Logistics: A Machine Learning Approach. CEUR Workshop Proceedings, Vol. 4163, 2026, p. 182-191. URL: https://ceur-ws.org/Vol-4163/paper16.pdf