An Intelligent Flood Forecasting and Early Warning Framework for Ungauged Watersheds Ref.No.SSTCRC2679
1. Introduction
The overall objective of this research is to develop and evaluate an intelligent flood forecasting and early warning system for ungauged watersheds by integrating hydrological models, remote sensing observations, and deep learning techniques under present and future climate conditions.
Specific Objectives:
- Develop a flood prediction framework for ungauged basins (Estimate streamflow and flood characteristics where no historical discharge measurements exist.)
- Integrate multi-source remote sensing datasets (Use satellite rainfall, soil moisture, land use, DEM, vegetation, and water extent products.)
- Combine physical hydrological modeling and AI (Utilize process-based models (e.g., SWAT, HEC-HMS, VIC, WRF-Hydro) together with deep learning models (LSTM, Transformer, CNN-LSTM).)
- Improve flood forecasting lead time and accuracy (Predict flood events several hours or days in advance.)
- Assess future flood risk (Use climate projections (CMIP6 scenarios such as SSP1-2.6, SSP2-4.5, SSP5-8.5).)
- Design an operational early warning framework (Generate flood alerts and risk maps for disaster management agencies.)
Existing studies often use Hydrological models only, Deep learning only andCurrent climate only but this study combines Ungauged watershed prediction, Multi-source remote sensing, Hybrid hydrological-AI modeling, Future climate projections and Early warning decision support. The combination of all five components is still relatively uncommon.So, There are many related studies, though not always integrating all components.
2. Research Progress
At the first step of this research (data collecting)
3. Required Cooperation
At a minimum, the project should involve A hydrologist, A remote sensing/GIS specialist, An AI/deep learning researcher, A climate scientist, A meteorological or water-resources agency, A disaster-management stakeholder.
On that basis, the biggest need for working on this project is securing its financial requirements.
4. Benefits
The project will deliver a hybrid flood forecasting and early warning framework capable of predicting flood events in ungauged watersheds using satellite observations, hydrological simulations, and deep learning algorithms. The system is expected to improve forecasting accuracy, extend warning lead times, generate present and future flood risk maps under climate change scenarios, and provide an operational decision-support tool for disaster risk reduction and climate-resilient watershed management.
5. Outputs
-A novel hybrid flood forecasting framework integrating hydrological modeling, multi-source remote sensing, deep learning, and climate projections.
-High-resolution flood hazard and risk maps for ungauged watersheds.
-A validated deep learning model for flood prediction and early warning.
-A geospatial database of hydrological, remote sensing, and climate data.
-Future flood risk assessments under multiple climate change scenarios.
-An intelligent prototype early warning and decision-support system.
-Peer-reviewed journal publications, conference papers, and technical reports.
-Policy and watershed-management recommendations for climate-resilient flood risk reduction.