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The Role of Machine Learning in Post-Disaster Humanitarian Operations: Case Studies and Strategic Implications
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
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.
How to cite this paper
@article{1708461,
author = {Nnanna Kalu-MBA, Munashe Naphtali Mupa, Sylvester Tafirenyika},
title = {The Role of Machine Learning in Post-Disaster Humanitarian Operations: Case Studies and Strategic Implications},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {725-734},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708461.pdf},
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.},
month = {May},
}