GWFlowAI: A One-Step, Location-Based Artificial Intelligence (AI) Framework for Exploring Public Groundwater Datasets in India

Groundwater assessment in India makes extensive use of publicly available datasets, including long-term monitoring well records, hydrogeological maps, and derived spatial products. However, accessing and interpreting these datasets typically involves multiple software tools, manual data extraction, and specialist expertise in GIS and hydrogeology, limiting their usability for broader user groups.

GWFlowAI is a location-based Artificial Intelligence (AI) framework designed to enable exploratory groundwater analysis through a single user input step: the entry of an address or geographic location. The system performs automated geocoding and spatial buffering to identify relevant administrative units and hydrogeological features. Public groundwater datasets are retrieved and harmonized using standardized coordinate reference systems and metadata-aware preprocessing pipelines.

The framework follows an agent-based architecture, in which specialized Artificial Intelligence agents are responsible for tasks such as data retrieval, geospatial processing, time-series analysis, and result interpretation. Time-series analysis methods are used for groundwater level trend detection, including statistical smoothing and change-point identification, while spatial analysis methods such as interpolation and zonal statistics are applied to characterize regional groundwater conditions. AI agents assist with workflow orchestration, analytical query interpretation, and generation of human-readable summaries, while core numerical and geospatial computations remain explicit and reproducible.

GWFlowAI is intended to support multiple user groups, including researchers, consultants, planners, students, and policy practitioners, enabling consistent access to the same public datasets at varying analytical depths. To the authors’ knowledge, this represents an early effort in India to provide a single-entry, integrated Artificial Intelligence (AI) workflow for groundwater data exploration based entirely on public datasets. The paper presents the system architecture, analytical methods, and representative outputs, and discusses limitations related to data resolution, uncertainty, and spatial coverage.