Cloud Burst Prediction System

Abstract

The Cloudburst Forecasting System has been created to enhance the precision in cloudburst forecasting. By utilizing AI models, this system processes weather reports and satellite and radar imagery to gauge the potential for an impending cloudburst and issues adequate early warnings. Improved accuracy ensures less life and property being threatened. Having GIS technology support will assist in equitably allocating resources since decision-makers can then factor in some physical attributes of housing stock that influence the vulnerabilities of the affected group. This would further lead to the advanced planning and resource development at both self-help and community levels so that the response teams can undertake the forthcoming disaster response after the occurrence. Some positive implications include public preparedness drive, optimum use of resources, and a comprehensive approach to financial loss mitigation. However, the bigger impediments are going to continue into present weather models/systems, inadequate data for cloudburst events, along with computational power being the field evermore.

Introduction

Advanced weather forecasting generates massive, complex data on factors like temperature, humidity, and cloud density, which can be difficult to process quickly for real-time decision-making, especially for sudden events like cloudbursts. Traditional methods are slow, error-prone, and lack scalability. Modern AI and machine learning enable more accurate, faster, data-driven weather predictions, but many current solutions are limited by specialization, lack of real-time integration, and poor user interfaces.

This work proposes an AI-based, web-accessible cloudburst prediction system with the following features:

• Collects real-time and historical weather data from GeoNames and OpenWeatherMap APIs.

• Uses a Random Forest machine learning model trained on key weather parameters to classify cloudburst risk as high or low.

• Stores and manages data securely in a PostgreSQL database.

• Offers an API and React-based interactive dashboard for users to view weather conditions, risk levels, and statistics.

• Supports continuous model improvement through feedback loops.

• Deployable as a scalable, cloud-hosted pipeline.

The system addresses gaps in existing forecasting by providing localized, real-time, interpretable, and user-friendly predictions at the city level. It improves early warning capabilities, helping agencies and individuals prepare for extreme weather events with better accuracy and timeliness.

Conclusion

The process outlined comprises product design, development, and deployment for the Cloudburst Risk Dashboard using the latest web technology and bringing in real-time data visualization and cloud deployment. The system presents React frontend, Flask as backend API, and Power BI for enterprise-grade visual analytics to render an interactive tool for cloudburst risk monitoring that is user friendly. The dashboard presents users the ability to see incoming weather information, to see risk level distributions via visualizations, and to engage with the BI reports in context. Hosting on AWS EC2 ensures global availability and remote monitoring capability. Although this system presently works with mock data, it sets a very strong foundation for any enhancements that will be made in the future: real-time weather API integration, predictive analysis with machine learning, and alerting automation, just to name a few. Overall, the project supplements the fledgling body of disaster risk visualization and serves as a foundation for more responsive data-driven environmental monitoring systems.

References

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Copyright

Copyright © 2025 Amruth Raj P, Katta Vinod Kumar, K Vishnu Vardhan, Nisha L, Rajeshwari C Raikar, Rajan Thangamani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.