AI-Driven Multi-Source Disaster Response System Using Machine Learning

DOI

10.2991/978-94-6239-713-2_51How to use a DOI?

Keywords

disaster management; early detection; machine learning; natural disasters; disaster response; deep learning

Abstract

Natural disasters such as floods, earthquakes, and landslides have become a global concern due to their destructive effects on the environment. These events have become more frequent due to massive climate change, which demands the need for an automated system that can analyze these effects and manage them efficiently. This study presents a system designed for disaster response that will analyze disaster management approaches and integrate information from diverse data streams, such as aerial imagery, historical disaster images, and social media disaster tweets. The system combines different machine learning (ML) models, such as supervised learning (SVM, LR, Naïve Bayes, and LSTM), to process data streams for identifying natural hazards. This utilises advanced technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) to identify the disaster events, classify them, and filter out misleading information. Used Natural Language Processing (NLP) to analyze textual data from online platforms and Computer Vision (CV) to examine visual evidence from images. The evaluation gives the idea that the proposed dashboard-based multi-source approach gives better results than traditional single-source systems in detecting accuracy and optimization. The system also used Flask-API as a database to store the outputs. Also, the system provides an interactive dashboard to deliver clear insights about the data accuracy. These tools will help emergency authorities and rescue teams to track ongoing incidents and evaluate risk zones. In the long run, this system will help in early rescue operations, reduce damage and save more lives.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aman Dagar
AU  - Nidhi Sharma
AU  - Deeya Joshi
AU  - Parth Chauhan
AU  - Gagandeep Raghav
AU  - Akshit Malik
AU  - Rishu Barak
PY  - 2026
DA  - 2026/06/25
TI  - AI-Driven Multi-Source Disaster Response System Using Machine Learning
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 688
EP  - 703
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_51
DO  - 10.2991/978-94-6239-713-2_51
ID  - Dagar2026
ER  -