PS 26070SOFTWARESmart EducationModerate Scope

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.

Ministry of Earth Sciences (MoES)India Meteorological Department
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30-Second Plain English Summary

Dense winter radiation fog across the Indo-Gangetic Plains (Delhi, Punjab, UP, Bihar) disrupts hundreds of flights, passenger trains, and highway freight operations daily with low-visibility conditions (<50 meters) that existing numerical models fail to forecast accurately at sub-kilometer scales. Build an AI/ML-based high-resolution fog and visibility nowcasting system combining satellite land surface temperature, boundary layer humidity sensors, and Ceilometer backscatter to forecast Runway Visual Range (RVR) up to 12 hours in advance.

5-Dimension Strategic ScorecardOverall Score: 3.8 / 5.0
Innovation
4.2 / 5
36h Feasibility
4.3 / 5
Uniqueness
3.5 / 5
Jury Appeal
3.5 / 5
Tech Depth
3.6 / 5
Recommended System Architecture Pipeline
INSAT-3D Night Fog Rasters + Airport METAR/RVR -> Boundary Layer Thermodynamics ML Engine -> Koschmieder Optical Extinction Converter -> Aviation & Rail Decision Portal
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Official Government Problem Description
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.
AI & PPT Citation Format

Smart India Hackathon 2026 Problem Statement PS-26070: "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.", Ministry: Ministry of Earth Sciences (MoES). Strategy & Architecture via SIH ONE (https://sihone.pages.dev/ps/26070)

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