SIH26025: Development of an AI-enabled Low Cost Real Time Mine Subsidence Monitoring, Pred — SIH 2026 | Wynit

SIH26025: Development of an AI-enabled Low Cost Real Time Mine Subsidence Monitoring, Pred — SIH 2026 | Wynit

What they'll ask you

12

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Exact historical subsidence datasets for model training?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific thresholds for tilt, vibration, and displacement that constitute an emergency?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

9 morethe ones a panel gets to if there is time

The brief asks for sensor node hardware. How would you build that?
Decoded from SIH26025 itself: Build low-cost smart sensor nodes using readily available hardware like Arduino, ESP32, or Raspberry Pi. The brief asks for it by name.
The brief asks for wireless mesh network. How would you build that?
Decoded from SIH26025 itself: Connect sensor nodes using a wireless mesh communication network such as LoRa, Zigbee, or Wi-Fi mesh. The brief asks for it by name.
The brief asks for ai anomaly detection. How would you build that?
Decoded from SIH26025 itself: Implement machine learning models to identify abnormal deformation patterns and predict subsidence zones. The brief asks for it by name.
The brief asks for gis dashboard. How would you build that?
Decoded from SIH26025 itself: Create an interactive web or mobile dashboard with GIS visualization for live deformation maps and risk zones. The brief asks for it by name.
The brief asks for early warning alerts. How would you build that?
Decoded from SIH26025 itself: Configure an automated notification system that sends alerts through SMS, email, or app notifications. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check if your prototype uses low-cost hardware like ESP32 or Arduino, demonstrates wireless mesh communication between nodes, uses AI for anomaly detection, and includes a GIS-based dashboard with automated alerts.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (479 words)

Background: Surface subsidence caused by underground coal mining poses significant risks to nearby communities, public infrastructure, agricultural land, forest areas, and the surrounding environment. In India, subsidence monitoring is still largely dependent on conventional field observations, periodic surveys, and post facto damage assessments, which often fail to provide timely warning before critical ground failure occurs. There is a strong need for an indigenous, low cost, intelligent, and real time monitoring solution capable of detecting early signs of ground movement and enabling proactive risk mitigation. Such a system should be affordable, scalable, and deployable across Indian underground coal mines using widely accessible technologies, thereby supporting the national vision of smart and sustainable mining. Description: The problem envisages development of an AI-enabled smart mine subsidence monitoring and early warning platform based on a localized wireless surface mesh sensor network deployed above underground mine panels. The proposed solution involves installing a distributed network of low cost smart sensor nodes across the surface over the underground mining area. Each node may be equipped with sensors such as: • tilt/inclination sensors, • vibration sensors, • displacement/stretch sensors, • crack detection sensors, • optional low cost positioning modules. These nodes will communicate through a wireless mesh communication network (such as LoRa/Zigbee/Wi-Fi mesh), enabling continuous real time monitoring of micro ground movements over the mine panel. The system should continuously detect: • abnormal ground tilt, • change in relative distance between nodes, • early crack initiation, • unusual vibration signatures, which may indicate the onset of subsidence. Using Artificial Intelligence / Machine Learning, the platform should: • identify abnormal deformation patterns, • predict possible subsidence zones, • estimate severity and progression, • generate automated early warning alerts, • support timely operational decisions. The solution should be robust, low power, scalable, and suitable for Indian geo-mining conditions. Expected Solution: A web/mobile enabled intelligent mine subsidence monitoring platform integrating IoT, wireless mesh networking, AI, and GIS technologies for: • development of low cost smart sensor nodes using readily available hardware platforms (e.g., Arduino/ESP32/Raspberry Pi); • deployment of a localized wireless mesh network over underground mine panels for continuous surface deformation sensing; • real time monitoring of tilt, displacement, vibration, and crack initiation; • AI/ML-based anomaly detection and subsidence prediction using live and historical data; • GIS based visualization of live deformation maps and risk zones; • automated early warning alerts through SMS/email/mobile app notifications; • interactive dashboards for mine operators, planners, and regulators; • offline capability with periodic cloud synchronization; • scalable deployment across multiple underground coalfields. The proposed solution must be low cost, easy to deploy, energy efficient, scalable, and student prototype friendly, while enabling a Made in India smart mining safety solution for sustainable underground coal mining. Now your problem statement has a clear unique innovation hook: 'Wireless Surface Mesh Network for Real Time Subsidence Detection' that is what will differentiate it from generic AI proposals.