Financial Risk and Customs Control in Humanitarian Water Logistics: A Machine Learning Approach.

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dc.contributor.authorDumanska, Ilona
dc.contributor.authorPavlova, Olga
dc.contributor.authorRabcan, Jan
dc.contributor.authorMelnyk, Alona
dc.contributor.authorKharun, Olena
dc.date.accessioned2026-02-17T14:26:40Z
dc.date.available2026-02-17T14:26:40Z
dc.date.issued2026-02-07
dc.description.abstractThis 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.
dc.identifier.citationDumanska 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
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/20789
dc.language.isoen
dc.publisherCEUR
dc.subjectmachine learning
dc.subjectfinancial risk
dc.subjectcustoms control
dc.subjectWaterWayfinder
dc.subjectGIS
dc.subjectmobile application
dc.titleFinancial Risk and Customs Control in Humanitarian Water Logistics: A Machine Learning Approach.
dc.typeСтаття
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Кафедра міжнародних економічних відносин