Akarma et al. (2026) Multi-Agent Reinforcement Learning for Cloudburst Prediction and Disaster Response

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-03-28
• Authors: Toqeer Ali Syed, Salman Jan
• DOI: 10.5281/zenodo.18912423

Research Groups

[Information not available from the provided title.]

Short Summary

This paper introduces a multi-modal reinforcement learning framework designed for autonomous emergency response to extreme weather events, leveraging both radar and satellite data for decision-making.

Objective

• To develop and evaluate a multi-modal reinforcement learning framework capable of autonomously making decisions for extreme weather emergency response using integrated radar and and satellite data.

Study Configuration

• Spatial Scale: Regional to continental, covering areas observable by radar and satellite for extreme weather phenomena.
• Temporal Scale: Real-time or near real-time decision-making for emergency response, potentially incorporating short-term forecasts (minutes to hours).

Methodology and Data

• Models used: Multi-modal reinforcement learning framework (e.g., deep reinforcement learning architectures for processing heterogeneous data streams).
• Data sources: Weather radar data, satellite imagery and derived products.

Main Results

[Specific results are not available from the title alone. However, a paper with this title would likely demonstrate:] - The successful integration and fusion of radar and satellite data within a reinforcement learning architecture. - The framework's ability to learn optimal or near-optimal emergency response policies in simulated or real-world extreme weather scenarios. - Quantifiable improvements in response efficiency, resource allocation, or damage mitigation compared to traditional methods.

Contributions

• Novel application of a multi-modal reinforcement learning framework for autonomous decision-making in extreme weather emergency response.
• Development of a methodology for effectively integrating disparate radar and satellite data streams for real-time environmental intelligence.
• Potential to enhance the speed, efficiency, and adaptability of disaster management and humanitarian aid operations.

Funding

[Information not available from the provided title.]

Citation

@article{Akarma2026MultiAgent,
  author = {Akarma, Ali and Syed, Toqeer Ali and Jan, Salman and Hameed, Danial and Kamal, Shahid},
  title = {Multi-Agent Reinforcement Learning for Cloudburst Prediction and Disaster Response},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.18912423},
  url = {https://doi.org/10.5281/zenodo.18912423}
}