Section / TensorFlow Rainfall Forecast

All-data rainfall analytics and fast TensorFlow LSTM pilot forecast

Combine stored NetCDF/CSV rainfall files, manual uploads, full-period filtering, QA/QC, graph-first visualization, and optional fast LSTM-style forecasting.

Recommended Algorithms
SCS-CN rainfall-runoff Unit hydrograph / response routing GEOGLOWS and GloFAS forecast adapters CWC gauge and altimetry validation Return-period and exceedance screening
Data APIs & Inputs
HydroBASINS/HydroSHEDS MP basin shapefiles CHIRPS/GPM/IMD rainfall CWC sites GEOGLOWS/GloFAS
Related Modules
Rainfall AI Lab

From NetCDF archive to fast flood-risk forecast

Load the full available IMD-style rainfall record first, then optionally run a capped TensorFlow LSTM pilot forecast without slowing normal graph review.

Default Mode All available data Monthly aggregation, TensorFlow off until selected
01SourceStored NetCDF/CSV or manual upload
02PrepareSort, combine, dedupe, aggregate
03VisualizeGraph loads before model forecast
04ForecastFast LSTM/proxy pilot output
Data Preparation

Repository and period controls

Manual data input CSV / NetCDF / NC4 Expected columns: date/time and rainfall/precipitation. NetCDF support depends on server xarray availability.
Fast Model Preset

Demo-safe LSTM parameters

Click inside India/Madhya Pradesh to plot the nearest 0.25 degree IMD rainfall grid cell.

Extreme Rainfall Events

S.No. District Year Date Rainfall (mm)
Select a point or load analysis to populate district-wise annual extreme events.
Analytics Output

Rainfall signal, risk band, and forecast

Load the module to view all-data rainfall graph, QA/QC, and optional forecast.

Recordsn/aselected rows
Total Rainfalln/aperiod sum
Wet Daysn/a> 2.5 mm
Max Dailyn/aextreme signal
Risk Bandn/ascreening score
TensorFlown/aruntime status
Historical Rainfall All available period
Pilot Forecast Forecast disabled until LSTM is selected