India’s 22,965 groundwater wells: what machine learning can and cannot learn from them
CGWB has monitored wells quarterly since 1969. The cadence makes the data strong for contamination mapping and weak for short-term forecasting.
- CGWB data covers 15,259+ monitoring locations and 22,965+ observation wells, sampled quarterly since 1969.
- Four readings a year suit spatial models (where is arsenic or fluoride likely?) better than temporal ones (what happens next month?).
- Pairing wells with satellite rainfall such as CHIRPS can fill the gaps between quarterly readings.
The Central Ground Water Board runs one of the longest groundwater monitoring programmes anywhere. Its portal publishes data from 15,259+ monitoring locations, including arsenic, nitrate, fluoride and uranium, with quarterly monitoring from 22,965+ observation wells.
Where the data is strong
Dense spatial coverage is ideal for classification and interpolation. A model can learn which combinations of geology, depth and land use go with high fluoride or arsenic, then flag villages without a nearby test well. That is a direct public-health use.
Where it is weak
Four samples a year cannot resolve how a water table responds to a single monsoon week. Any model that claims monthly groundwater forecasts from CGWB data alone is interpolating, and should say so.