# Agentic AI Framework for Cloudburst Prediction and Coordinated Response

arXiv: https://arxiv.org/abs/2511.22767 · HTML: https://mlsanity.com/paper/2511.22767/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Deep Learning for Day Forecasts from Sparse Observations](https://mlsanity.com/paper/2306.06079/) (2023)
- [Artificial Intelligence for Atmospheric Sciences: A Research Roadmap](https://mlsanity.com/paper/2506.16281/) (2025)

## Built on by (most cited first, 6 of 6)

- [Toward Trustworthy Agentic AI: A Multimodal Framework for Preventing Prompt Injection Attacks](https://mlsanity.com/paper/2512.23557/) (2025)
- [A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines](https://mlsanity.com/paper/2512.20985/) (2025)
- [Agentic AI Framework for Smart Inventory Replenishment](https://mlsanity.com/paper/2511.23366/) (2025)
- [Governance-Constrained Agentic AI: Blockchain-Enforced Human Oversight for Safety-Critical Wildfire Monitoring](https://mlsanity.com/paper/2604.04265/) (2026)
- [Use of AI Tools: Guidelines to Maintain Academic Integrity in Computing Colleges](https://mlsanity.com/paper/2604.11111/) (2026)
- [Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making](https://mlsanity.com/paper/2604.27753/) (2026)

## Abstract (arXiv)

The challenge is growing towards extreme and short-duration rainfall events like a cloudburst that are peculiar to the traditional forecasting systems, in which the predictions and the response are taken as two distinct processes. The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence, which combines sensing, forecasting, downscaling, hydrological modeling and coordinated response into a single, interconnected, priceless, closed-loop system. The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle, and use the intelligence of weather prediction to become real-time decision intelligence. Comparison of multi-year radar, satellite, and ground-based evaluation of the northern part of Pakistan demonstrates that the multi-agent configuration enhances forecast reliability, critical success index and warning lead time compared to the baseline models. Population reach was maximised, and errors during evacuation were minimised through communication and routing agents, and adaptive recalibration and transparent auditability were provided by the embedded layer of learning. Collectively, this leads to the conclusion that collaborative AI agents are capable of transforming atmospheric data streams into practicable foresight and provide a platform of scalable adaptive and learning-based climate resilience.