Agentic AI Framework for Cloudburst Prediction and Coordinated Response

TL;DR

This paper presents an agentic AI framework that integrates sensing, forecasting, and response for cloudburst prediction, demonstrating improved reliability and adaptive response in atmospheric event management.

Contribution

It introduces a novel multi-agent AI system that unifies atmospheric monitoring, prediction, and response, enhancing climate resilience through real-time, adaptive decision-making.

Findings

Enhanced forecast reliability and warning lead time.

Maximized population reach and minimized evacuation errors.

Embedded learning layer enables adaptive recalibration.

Abstract

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.