Disaster Management
Browse and strategize across all 29 official problem statements for Disaster Management. Complete with verified datasets, tech stack blueprints, and jury defense playbooks.
Problem Statements List (29)
Match My TeamAI-Based early warning and landslide Risk Monitoring System in NER
Landslides, flash floods, and slope failures in the North Eastern Region isolate hill communities and delay disaster response due to purely reactive manual reporting. Build an AI-driven early warning platform that ingests rain gauge telemetry, soil moisture sensors, and satellite slope data to broadcast real-time localized warnings via SMS and offline-capable PWA to village administrations.
Automated lntegration and lntelligent Harmonization of Multi-source Geospatial Data for urban Land Record Management.
Urban land records in India are scattered across incompatible departmental silos—revenue records, municipal GIS tax layers, utility pipe networks, and drone surveys all have conflicting coordinates and schemas. Build an AI-driven geospatial harmonization platform that automatically geo-references, conflates, and topologically syncs multi-source spatial layers into an authoritative cadastral database.
Application of Geospatial Techniques for visualization and analysis to interpret Geo-Coded lmages to enhance watershed Development Outcomes.
Watershed development monitoring in rural India relies on manual documentation while thousands of geotagged field photos remain unlinked to satellite observations. Build an integrated geospatial watershed analytics platform combining 30-meter SRISHTI-DRISHTI / Landsat satellite imagery with field-collected geocoded photos to track water conservation check-dam impacts, vegetation greening (NDVI), and soil moisture changes over time.
Development of an AI-enabled Low Cost Real Time Mine Subsidence Monitoring, Prediction and Early Warning System for Underground Coal Mines in India
Underground coal mining causes unexpected surface ground subsidence, damaging roads, railway tracks, and rural villages located above active extraction panels. Build an indigenous, low-cost real-time subsidence monitoring platform using a wireless surface mesh sensor network (ESP32 + LoRa/Zigbee) measuring ground tilt, extensometer displacement, and micro-vibrations to forecast ground collapse before catastrophic failure.
Dynamic Forecast of Expected Time of Arrival (ETA) for Coaching Trains
Indian Railways National Train Enquiry System (NTES) uses static historical section running times to forecast train Arrival Times (ETA), leading to inaccurate passenger updates during unexpected yard congestion and weather delays. Build a real-time dynamic ETA forecasting engine combining GPS locomotive telemetry, section signaling occupancy, line capacity bottleneck models, and weather feeds to predict station arrival times with minute-level precision.
Automated High-Current Short-Circuit Test System for IEC 60898-1:2015 MCB Compliance.
Testing Miniature Circuit Breakers (MCBs) under international standard IEC 60898-1:2015 requires dangerous high-current short-circuit tests (up to 10kA) with precise point-on-wave switching that manual laboratory setups struggle to capture accurately. Build an automated high-current short-circuit testing and data acquisition (DAQ) platform with microsecond synchronous point-on-wave firing, optical arc voltage monitoring, and automated test certificate generation for BIS laboratories.
A digital platform to crowdsource societal challenges and facilitate collaborative problem solving through universities and industry partnerships
Citizens in Jharkhand face local civic and environmental challenges (drinking water contamination, road potholes, illegal mining, defective streetlights) with no transparent bridge connecting grassroots problems to university innovators and government funding. Build a crowdsourced civic challenge and co-innovation platform where citizens post geotagged community issues, university student teams submit prototype solutions, and municipal departments track project implementation.
Digital Platform for efficient remote management of Indian Antarctic Research Stations
Indian Antarctic Research Stations (Maitri and Bharati) operate thousands of kilometers away in sub-zero isolation, where equipment telemetry (diesel generators, HVAC life support, water purification, scientific instruments) is monitored on fragmented local computers without unified remote digital twin oversight from NCPOR Goa. Build a centralized Digital Platform for remote management of Indian Antarctic Research Stations with satellite IoT telemetry synchronization, predictive life-support maintenance, and emergency response coordination.
WeatherGPT: Conversational AI for Weather Forecasting, Alerts, and Climate Information
Citizens and farmers in India struggle to understand complex technical weather bulletins (convective available potential energy, isobars, hectopascals) issued by the India Meteorological Department (IMD), while static mobile apps fail to answer specific local questions. Build 'WeatherGPT'—a conversational AI weather intelligence platform combining grounded IMD/NCMRWF forecasts, live Doppler radar feeds, and regional language voice LLMs to provide hyper-localized weather advice, agricultural spray advisories, and disaster safety alerts.
National Weather Big Data Analytics Platform
The Ministry of Earth Sciences (MoES) operates thousands of weather observation platforms (Doppler Weather Radars, Automatic Weather Stations, satellites, lightning sensors, climate models) that generate petabytes of high-velocity data in incompatible formats, creating analytical bottlenecks for forecasters. Build a National Weather Big Data Analytics Platform that provides high-throughput streaming ingestion, distributed raster query engines, and predictive extreme weather anomaly discovery for IMD meteorologists.
AI/ML-Based Integrated heavy rainfall Early Warning and Inundation Prediction System using Satellite, Radar, observational Weather and numerical weather prediction model data.
Extreme monsoon downpours in major Indian cities (Mumbai, Chennai, Bengaluru, Delhi) cause catastrophic urban flooding within 60 minutes due to outdated stormwater drain capacities and lack of integrated hydraulic modeling. Build an AI-driven integrated heavy rainfall early warning and 2D urban inundation prediction system coupling Doppler weather radar rainfall nowcasts with municipal drainage networks to forecast street-level water logging depth.
AIML based Nowcasting of thunderstorm and lightning using atmospheric observation including multiple radars, satellite, lightning and model data.
Severe thunderstorms, squalls, and cloud-to-ground lightning strikes kill over 2,500 rural farmers and laborers annually in India due to sudden onset and lack of localized nowcasting. Build an AI/ML-based nowcasting and early warning platform for IITM/IMD that ingests Lightning Location Network (LLN) pulses, Doppler radar reflectivity, and INSAT-3D cloud-top temperatures to forecast thunderstorm cell paths and lightning strike hazard zones 30-90 minutes in advance.
AI/ML-Based Intelligent Anomaly Detection for Automatic Weather Stations (AWS)
The India Meteorological Department (IMD) operates a nationwide network of 1,200+ Automatic Weather Stations (AWS), but field sensors suffer from sensor drift, clogged rain gauge funnels, solar battery depletion, and bird roosting, leading to erroneous data entering national numerical weather prediction models. Build an AI/ML-based intelligent anomaly detection and automated quality control platform for IMD that flags stuck sensors, physical drift, and temporal-spatial inconsistencies in real time.
Downscaling of weather forecast from Block level to Panchayat level: Inferring high-resolution plots/ data/ information from low-resolution plot /data /information /variables for agro-meteorological advisory services.
IMD currently produces Numerical Weather Prediction (NWP) forecasts at coarse 12 km grid resolution (Block level), but rural farmers need accurate rain, temperature, and wind forecasts at the 1-3 km Gram Panchayat / village level where localized terrain, microclimates, and water bodies create severe weather variations. Build an AI/ML statistical downscaling platform combining high-resolution topography (DEM), land cover, and localized AWS observations with NWP model outputs to generate Panchayat-level 1 km hyper-local forecasts.
AI-Driven Hyper-Local Early Warning System for Severe Weather Nowcasting
Severe local convective storms (thunderstorms, squalls, microbursts) develop and dissipate within 1 to 2 hours over small 5-10 km areas, evading traditional numerical models. Build an AI-driven hyper-local severe weather nowcasting system combining dual-polarization Doppler weather radar volumetric scans, INSAT-3D rapid-scan infrared imagery, and surface AWS networks to generate automated 0-2 hour convective storm cell tracks and wind gust nowcasts at 500m resolution.
AI-Driven Spatio-Temporal Tracking of Extreme Weather Anomalies in Medium-Range Forecasts
National Centre for Medium Range Weather Forecasting (NCMRWF) runs global and regional Numerical Weather Prediction (NWP) ensemble models (NCUM) producing massive multi-dimensional forecast grids, where detecting emerging extreme weather anomalies (monsoon low-pressure depressions, heat dome patterns, atmospheric rivers) across 10-day forecast horizons requires tedious manual analysis. Build an AI-driven Spatio-Temporal Extreme Weather Anomaly Tracker for NCMRWF that uses 3D Convolutional Neural Networks and extreme value statistics to automatically identify, track, and rank extreme meteorological anomalies across ensemble forecast members.
AI-Based Forecast Bust Detection for Medium-Range Weather Forecasts
Numerical Weather Prediction (NWP) models occasionally suffer from catastrophic 'forecast busts'—where forecasted weather drastically fails in reality (e.g. predicting clear skies when a torrential downpour occurs, or missing the track of a monsoon depression) due to initial condition errors or convection parameterization failures. Build an AI-based Forecast Bust Early Detection and Confidence Scoring platform for NCMRWF that analyzes real-time observation discrepancies, ensemble spread-error relationships, and atmospheric instability indicators to flag low-confidence forecast runs before public release.
Regime-Aware AI Post-Processing of Monsoon Rainfall Forecasts
Indian Summer Monsoon rainfall exhibits distinct large-scale atmospheric regimes (Active vs Break phases, Madden-Julian Oscillation MJO phases, El Niño / IOD states) where raw Numerical Weather Prediction (NWP) models suffer from regime-dependent systematic biases (e.g. overestimating dry spells during break phases or mislocating the monsoon trough). Build a Regime-Aware AI Post-Processing and Bias-Correction platform for NCMRWF that uses unsupervised regime clustering and physics-guided neural networks to dynamically correct monsoon rainfall forecasts based on active large-scale climate states.
Air PollutionWeather Coupled Forecasting System (Delhi NCR Focus)
Severe winter air pollution in Delhi-NCR (hazardous PM2.5 / PM10 AQI > 450) is driven by an explosive combination of stubble burning emissions, vehicular exhaust, and stagnant meteorological conditions (nocturnal temperature inversions, calm surface winds <1 m/s, low boundary layer height). Build a coupled Air Pollution-Weather Forecasting platform for IITM/SAFAR combining WRF-Chem atmospheric chemistry models, satellite fire radiative power (FRP), and deep learning to forecast hyper-local AQI and stubble smoke dispersion 72 hours in advance.
Extreme Heatwave Early Warning and Human Thermal Stress Index
Severe summer heatwaves across India claim thousands of lives and cause massive labor productivity loss, while standard temperature forecasts fail to capture the lethal physiological threat of 'Wet-Bulb Temperature' (combined extreme heat and high humidity where the human body cannot cool down through sweating). Build an AI-driven Extreme Heatwave Early Warning and Human Thermal Stress Index platform for IMD that computes localized Wet-Bulb Globe Temperature (WBGT), Universal Thermal Climate Index (UTCI), and neighborhood-level heat vulnerability heatmaps.
Convective scale nowcasting for Thunderstorms, Hail & Cloudbursts (06 hr)
Severe localized convective cloudbursts, severe hailstorms, and squall lines across the Himalayas and central plains develop within 30 minutes, causing sudden flash floods and severe crop destruction before traditional numerical models can react. Build an AI-driven convective scale nowcasting platform for IMD (0-6 hour forecast horizon) that assimilates 3D Doppler radar reflectivity grids, INSAT-3D rapid-scan cloud-top cooling rates, and GNSS Integrated Water Vapor (IWV) to predict cloudburst initiation and hail size with sub-kilometer precision.
Urban Flood Nowcasting System (Drainage and Rainfall Coupling)
Indian metropolitan cities (Bengaluru, Chennai, Mumbai, Hyderabad) suffer chronic urban waterlogging where water accumulates in specific street intersections within 30 minutes of rain, but municipal corporations lack real-time visibility into drainage bottleneck points. Build an Urban Flood Nowcasting and Drainage Coupling System for IMD and Municipal Corporations that couples high-resolution Doppler radar rainfall nowcasts with municipal stormwater pipe GIS topologies to predict sub-catchment waterlogging depths and pump dispatch schedules.
Dam Break Inundation Modelling Using Hydrodynamic Modelling of any River
Catastrophic dam failures in India (due to cloudbursts, earthquakes, structural breach) cause devastating downstream flash floods and loss of life within hours, where disaster authorities lack real-time hydrodynamic breach simulation models to plan evacuations. Build an AI-Accelerated Dam Break Inundation and River Hydrodynamic Modeling Platform for NTRO combining dam breach breach-parameter physics (Froehlich / MacDonald formulas), 2D shallow water hydrodynamic solvers (HEC-RAS / LISFLOOD), and high-resolution DEMs to forecast downstream flood arrival times and evacuation corridors in real time.
A resilient, AI-powered environmental monitoring network that provides early detection, localized intelligence, and actionable alerts for floods, forest fires, pollution events, and other environmental hazards common in India, enabling authorities and communities to shift from reactive disaster response to proactive risk prevention.
Industrial complexes, national parks, and smart cities in India require continuous environmental monitoring (toxic gas leaks, wildfire smoke, air particulate matter, water runoff contamination), but existing sensor networks suffer from cellular single points of failure, solar battery depletion, and high maintenance costs. Build a Resilient, AI-Powered Autonomous Environmental Monitoring Mesh Network for Qualcomm Inc using Qualcomm RB5 / Snapdragon IoT edge gateways that orchestrates low-power LoRa/Zigbee sensor nodes, on-device anomaly detection, and self-healing mesh routing.
A field-deployable AI-powered Smart Farming Assistant that helps farmers detect crop diseases, pests, nutrient deficiencies, and irrigation needs at an early stage, while improving resilience against droughts, floods, heat waves, and other agricultural risks common in India. The solution should enable higher yields, lower input costs, more efficient water usage, and faster response to emerging threats through real-time on-device intelligence.
Smallholder farmers in India lose 25-40% of crop yields to unpredictable pest infestations, incorrect fertilizer application, and water stress, while existing agritech apps fail because they require constant high-speed cloud internet that is unavailable in rural fields. Build a Field-Deployable AI Smart Farming Assistant for Qualcomm Inc running 100% on-device on Qualcomm Snapdragon mobile platforms that performs instant offline crop disease diagnosis, soil nutrient recommendations, and vernacular voice advisories.
Intelligent Identification of Hazard-Based Red Zones, Carrying Capacity Assessment, and Immediate Relocation Needs for Vulnerable Habitations
Fragile Himalayan eco-sensitive pilgrimage towns (Joshimath, Kedarnath, Manali, Shimla) face catastrophic land subsidence, slope destabilization, and traffic gridlocks due to unregulated tourist influx exceeding local ecological carrying capacities. Build an AI-Driven Hazard-Based Red Zone and Dynamic Tourist Carrying Capacity Management Platform for the Ministry of Home Affairs (MHA / NDMA) that fuses InSAR satellite ground deformation, geotechnical slope stability models, and real-time mobile cell-tower crowd densities to dynamically regulate tourist entry permits and establish prohibited construction zones.
Flash Flood Prediction System for Hilly Regions using Multi-Source Data Theme
Mountainous Himalayan states (Himachal Pradesh, Uttarakhand, Sikkim) suffer devastating flash floods and debris flows caused by localized cloudbursts, glacial lake outbursts (GLOFs), and landslide dam failures (LLDAM) that wipe out downstream villages with zero warning. Build a Real-Time Flash Flood Prediction and Multi-Source Data Fusion Early Warning System for MHA / NDMA combining Doppler weather radar rainfall nowcasts, satellite hydrological soil saturation, GNSS water vapor surges, and ultrasonic river water-level IoT sensors to forecast flash floods 30-60 minutes in advance.
Student Innovation
AICTE Open Innovation: Disaster Management (Software) — Disaster response agencies in India during cyclones, floods, and earthquakes struggle with fragmented communication, delayed situational awareness, and chaotic resource distribution (food, boats, medical supplies) across affected districts. Build an AI-Powered Unified Disaster Situational Awareness, Crowdsourced SOS Triage, and Emergency Resource Logistics Platform for disaster management authorities (NDMA/SDRF) that aggregates satellite inundation rasters, social media distress pings, and optimizes rescue boat/vehicle routing.
Student Innovation
AICTE Open Innovation: Disaster Management (Hardware) — Flash floods and cloudbursts in remote Himalayan rivers and coastal creeks in India wipe out downstream settlements with zero advance warning because existing river level monitoring stations rely on grid power and fragile cellular towers that fail during heavy storms. Build an Autonomous Solar-Powered, Satellite/LoRa Connected Flash Flood River Gauging Station and High-Decibel Warning Siren Tower featuring non-contact 24GHz radar water-level sensing, satellite IoT messaging, and a 120 dB autonomous siren.