IndiaPublic sector2025 – ongoingMinistry of Jal Shakti
CWC short-range flood forecasting with LSTM models
CWC developed LSTM flood models for 28 stations and published forecasts through a live portal
Problem
Flood managers needed frequently refreshed water-level forecasts. Existing short-range forecasting relied on traditional statistical correlation methods.
Approach
CWC's Smart Water Resources Modelling Organisation developed LSTM-based models after station pilots. It created a graphical application so nominated officers could develop and test models without coding. A dedicated portal made forecasts available every three hours.
Results
Takeaway
The rollout included tools for officers to build station models. CWC did not define the accuracy measure behind its reported result.
Categories
- Size
- Large enterprise
- Industry
- Public sector
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Built in-house
- Evidence
- Company's own claim
Sources
Source summaries
CWC reported developing LSTM models for 28 stations with more than 80% accuracy. It described officer-facing modelling software and a portal publishing forecasts every three hours.
Full details are in the original sources.
