Frequently Asked Questions

Smart Bhujal builds AI-enabled decision-support systems for water — turning remote sensing, numerical modeling and custom software into decisions on groundwater, floods, irrigation and water networks. Founded by Dr. Jagadeesh Gaddam (PhD, IIT Tirupati), the team combines water science and software engineering to make water decisions data-driven.

A decision-support system brings your water data, models and analysis into one place so you can make faster, better-informed decisions. Instead of scattered reports and spreadsheets, a DSS connects the underlying data to interactive dashboards, scenarios and alerts — so planners, engineers and operators can see the current state, test options and act on the evidence.

We build across four DSS layers, combining whichever a problem needs:

• Remote Sensing & AI — extracting water insights from satellite and geospatial data
• Numerical Modeling — simulating groundwater, surface water and networks (MODFLOW, HEC-RAS, EPANET)
• Software & AI Agents — custom web platforms that make models and data usable
• Monitoring & Sensors — field measurements that keep the picture current

GWFlowAI is our AI-powered groundwater intelligence platform, built on public CGWB data. It offers groundwater level analysis, geological context, trend insights and location-specific intelligence — an example of the Software & AI Agents layer, making groundwater data usable for decisions rather than leaving it locked in raw tables.

We work with established methods across the four DSS layers:

• Numerical modeling — MODFLOW (groundwater), HEC-RAS (surface water and floods), EPANET (water distribution networks)
• Remote sensing & AI applied to satellite and geospatial datasets
• Modern web stacks for custom dashboards and decision-support software
• Field monitoring and sensors to keep models grounded in current conditions

We build on established, peer-reviewed modeling methods and public datasets such as CGWB groundwater data, calibrate models against observed measurements, and pair them with field monitoring so the picture stays current. Reliability depends on the data and calibration available for each site, so we scope and validate every system to the conditions on the ground rather than quoting blanket figures.

We combine CGWB groundwater data, remote sensing and numerical models (MODFLOW) to map aquifers, analyze trends and test management scenarios — so recharge, abstraction and monitoring can be planned with evidence rather than guesswork, and reviewed through dashboards your team can use.

Yes. We pair hydrologic and hydraulic modeling (HEC-RAS) with remote sensing to simulate flows, map flood extents and evaluate interventions, delivered through dashboards that make the results usable for planning and response.

Yes. For irrigation, we combine remote sensing and field data to inform scheduling and allocation decisions. For water distribution networks, we use hydraulic modeling (EPANET) to analyze pressure, demand and losses — turning network data into decisions operators can act on.

Most engagements start with the water decision you need to make. We scope the right mix of the four DSS layers — remote sensing, modeling, software and monitoring — build against your data, and deliver a system your team can keep using and extending, not a one-off report.

Yes — GWFlowAI, our groundwater intelligence platform, is free to use. It reflects our belief that better water decisions start with open access to data and analysis. For organizations that need a tailored decision-support system, we build custom solutions across the four DSS layers.

Yes. A decision-support system stays useful only if it stays current, so we support the data, models and software over time — updates, monitoring integration, and technical assistance — as your needs and datasets evolve.