CycloneSight AI Explanation SIH 2026
Descrição
PS ID:- SIH26070
PS TITLE:- To develop an Artificial Intelligence (AI) / Machine Learning (ML) based system for identification, classification, and prediction of different tropical cyclone patterns using multi-source satellite data.
CycloneSight AI is an AI-powered, Progressive Web Application (PWA) designed to improve tropical cyclone monitoring, prediction, early warning, and impact assessment using multi-source satellite and meteorological data.
The system continuously analyzes satellite imagery and available meteorological parameters to identify cyclone formation, track its evolution, estimate intensity, and detect Rapid Intensification (RI) at an early stage. Unlike conventional systems that primarily provide cyclone position and intensity forecasts, CycloneSight AI focuses on understanding how a cyclone is changing and what those changes mean for people and infras
tructure.
The proposed solution introduces a Cyclone Evolution Fingerprint that represents the changing characteristics of a cyclone over time. AI-based computer vision models analyze satellite imagery, while temporal models study the evolution of cloud structure, intensity, movement, and environmental conditions.
A key component is the Rapid Intensification Early-Warning Engine, which identifies unusual changes in cyclone characteristics and generates early alerts. The platform also uses Explainable AI (XAI) techniques such as Grad-CAM and SHAP to show why a model has generated a particular prediction instead of providing a black-box output.
CycloneSight AI further performs multi-source disagreement and uncertainty detection. When different data sources or models provide significantly different predictions, the system highlights the disagreement and communicates prediction confidence, helping users understand the reliability of the forecast.
The platform includes a Cyclone-to-Impact Translator that converts meteorological predictions into dynamic risk information for affected regions, including potential hazards such as strong winds, heavy rainfall, storm surge and flooding. Historical cyclone retrieval enables comparison with similar past events, while a Human-in-the-Loop mechanism allows meteorologists or authorized experts to provide feedback and improve future predictions.
Another important feature is “What Changed in the Last 3 Hours?”, which compares recent satellite observations with previous observations to highlight significant changes in cyclone structure and behavior.
CycloneSight AI aims to provide a unified, explainable and uncertainty-aware intelligence layer for disaster management authorities, meteorologists, researchers and communities, supporting faster and better-informed decision-making during tropical cyclone events.
PS TITLE:- To develop an Artificial Intelligence (AI) / Machine Learning (ML) based system for identification, classification, and prediction of different tropical cyclone patterns using multi-source satellite data.
CycloneSight AI is an AI-powered, Progressive Web Application (PWA) designed to improve tropical cyclone monitoring, prediction, early warning, and impact assessment using multi-source satellite and meteorological data.
The system continuously analyzes satellite imagery and available meteorological parameters to identify cyclone formation, track its evolution, estimate intensity, and detect Rapid Intensification (RI) at an early stage. Unlike conventional systems that primarily provide cyclone position and intensity forecasts, CycloneSight AI focuses on understanding how a cyclone is changing and what those changes mean for people and infras
tructure.
The proposed solution introduces a Cyclone Evolution Fingerprint that represents the changing characteristics of a cyclone over time. AI-based computer vision models analyze satellite imagery, while temporal models study the evolution of cloud structure, intensity, movement, and environmental conditions.
A key component is the Rapid Intensification Early-Warning Engine, which identifies unusual changes in cyclone characteristics and generates early alerts. The platform also uses Explainable AI (XAI) techniques such as Grad-CAM and SHAP to show why a model has generated a particular prediction instead of providing a black-box output.
CycloneSight AI further performs multi-source disagreement and uncertainty detection. When different data sources or models provide significantly different predictions, the system highlights the disagreement and communicates prediction confidence, helping users understand the reliability of the forecast.
The platform includes a Cyclone-to-Impact Translator that converts meteorological predictions into dynamic risk information for affected regions, including potential hazards such as strong winds, heavy rainfall, storm surge and flooding. Historical cyclone retrieval enables comparison with similar past events, while a Human-in-the-Loop mechanism allows meteorologists or authorized experts to provide feedback and improve future predictions.
Another important feature is “What Changed in the Last 3 Hours?”, which compares recent satellite observations with previous observations to highlight significant changes in cyclone structure and behavior.
CycloneSight AI aims to provide a unified, explainable and uncertainty-aware intelligence layer for disaster management authorities, meteorologists, researchers and communities, supporting faster and better-informed decision-making during tropical cyclone events.
Palavra-chave: meteorologia xai xai
