Competition Evaluators
Niamat Rai State Competition — pushed the project from a technical concept toward civic systems thinking. Their questions sharpened how the problem was framed.
Research Project 04 / Urban Infrastructure & Predictive Modelling
RAKṢITA
A predictive intelligence layer over Delhi's existing drainage infrastructure — using real government datasets, mathematical flood modelling, and real-time sensors to stop waterlogging before it starts. Built on actual CPCB, IMD, NIDM, and Delhi Drainage Master Plan data.
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Video coming soon — Higgsfield
01The Problem
From flooded streets to early warning — built on real Delhi data.
Delhi floods every monsoon. 11,400 metric tonnes of solid waste clogs drains annually. Government responses in 2019, 2020, 2021, 2023, and 2024 repeatedly failed. The Burari drain alone is a case study in systemic infrastructure collapse. The cost is measured in lives, livelihoods, and billions in damage — every single year.
02The Journey
Raksita (Sanskrit: protected) does not propose another structural fix. Instead, it builds a predictive intelligence layer over the infrastructure that already exists. The core insight: if you can predict flooding 2–4 hours before it becomes critical, authorities have enough time to act.
The system analysed catchment areas across 14 locations in Delhi. Surface runoff coefficients were calculated for both soil texture and surface composition — including precise calculations for the Burari Drain specifically. The data came from real government sources: CPCB, IMD, NIDM, and Delhi's Drainage Master Plan. This is not simulated data — it is the actual numbers the city runs on.
Hardware deployed includes rain gauge tipping bucket sensors and ultrasonic trash-level sensors for real-time data capture. The two-step logic: detect trash accumulation levels first, then trigger preemptive resource allocation before water levels become critical.
The project won 1st Position and the Best Team Award at the Shri R.S. Niamat Rai Rajpal Memorial Contest for Investigatory Projects in Mathematics, Springdales Schools, New Delhi, 2024. Presenting to competition evaluators taught me how to translate engineering systems into civic language — a skill as important as the system itself.
Teachers at Springdales
Mentored the mathematical modelling approach and helped refine the catchment area calculations and runoff coefficient methodology.
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Future urban infrastructure mentor or city planning collaborator — space reserved.
03The Build
Data Layer — Government Datasets
Real data from CPCB, IMD, NIDM, and Delhi's Drainage Master Plan. 14 catchment areas analysed. Surface runoff coefficients calculated for soil texture and surface composition.
Rain Gauge Tipping Bucket Sensors
Capture real-time rainfall intensity across monitored zones. Each tip of the bucket corresponds to a precise volume of rainfall — enabling continuous measurement.
Ultrasonic Trash-Level Sensors
Detect drain blockage levels before water accumulation becomes critical. Trash is the primary cause of drain failure — detecting it early is the key intervention point.
Intelligence Layer — Mathematical Model
Converts raw sensor data and historical runoff coefficients into flood risk predictions. Calculates precise flood thresholds and identifies at-risk zones hours in advance.
Action Layer — Alert System
Automated alerts to municipal authorities for preemptive resource allocation and urban planning intervention — before water levels become unmanageable.
04The Outcome
- Built a complete three-layer system: Data (government sources) → Intelligence (mathematical model) → Action (automated alerts).
- Analysed 14 real Delhi catchment areas using actual CPCB, IMD, NIDM, and Drainage Master Plan data — including precise runoff coefficients for the Burari Drain.
- 1st Position + Best Team Award at the Shri R.S. Niamat Rai Rajpal Memorial Contest for Investigatory Projects in Mathematics, Springdales Schools, New Delhi, 2024.
- Reframed the flooding problem from a structural engineering question to a predictive intelligence question — the city doesn't need new drains, it needs to know when existing drains will fail.
- Demonstrated that student-led civic innovation can operate with the same data rigour as professional urban planning.