Current Volume 8
The Role of Machine Learning in Post-Disaster Humanitarian Operations: Case Studies and Strategic Implications
Abstract
This article explores the transformative role of machine learning (ML) in enhancing post-disaster humanitarian operations. With the increasing intensity and frequency of natural disasters, ML provides a new approach to effective response, loss estimation, and efficient use of resources. In an analytical review of recent case studies and real-world applications such as flood forecasting, remote sensing of structural damage, and refugee settlement mapping, the study shows how ML coupled with big data, IoT, and satellite systems leads to better decision-making and increased operational efficiency. Disaster response architectures are discussed in the context of important AI models, including neural networks, decision trees, and deep learning frameworks, as well as ethical and sustainability concerns inherent in the humanitarian work based on data. The article highlights the necessity of ESG-conscious practices, solid policy frameworks, and alignment with SDGs. Strategic recommendations are offered to ensure fair and scalable deployment of ML technologies that would strengthen their ability to transform disaster resilience and humanitarian supply provision across the world.
Citations
IRE Journals:
Nnanna Kalu-MBA , Munashe Naphtali Mupa , Sylvester Tafirenyika
"The Role of Machine Learning in Post-Disaster Humanitarian Operations: Case Studies and Strategic Implications" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 725-734
IEEE:
Nnanna Kalu-MBA , Munashe Naphtali Mupa , Sylvester Tafirenyika
"The Role of Machine Learning in Post-Disaster Humanitarian Operations: Case Studies and Strategic Implications" Iconic Research And Engineering Journals, 8(11)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12