Abstract
The application of neural networks in disaster response is transforming the way emergency services make real-time decisions. This article explores the potential of neural networks to assist in disaster management by processing vast amounts of data quickly and accurately. Focusing on real-time decision-making, the article examines how neural networks can help predict disaster outcomes, optimize resource allocation, and enhance the overall effectiveness of emergency response efforts. Furthermore, it discusses the challenges and opportunities that arise when integrating neural networks into disaster response systems, including data quality, model accuracy, and the need for human oversight.
All articles published in the American Journal of Artificial Intelligence and Neural Networks (AJAINN) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Under this license:
-
Authors retain full copyright of their work.
-
Readers are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.
-
Proper credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated.
This open licensing ensures maximum visibility and reusability of research while maintaining author rights.