The Groundwater Query Agent is a district-based groundwater data retrieval system built using Fetch.AI's uAgents library. This agent allows users to input a district name and fetch detailed groundwater statistics, such as annual groundwater draft, replenishable groundwater resources, and the projected demand for future years. The data is sourced from a JSON file and presented in a user-friendly format to help users understand the groundwater availability and usage in specific districts.
Users can input the name of any district to retrieve the latest groundwater data. The agent automatically fetches and displays detailed groundwater statistics for the specified district.
Annual Domestic & Industrial Draft: Displays the amount of groundwater used for domestic and industrial purposes. Annual Irrigation Draft: Shows the amount of groundwater used for irrigation.
Total Annual Groundwater Draft: Provides the total groundwater usage for the district.
Replenishable Groundwater Resources: Displays the annual replenishable groundwater available.
Natural Discharge during Non-Monsoon: Shows the amount of groundwater naturally discharged during the non-monsoon season.
Net Groundwater Availability: Displays the total groundwater available for use. Projected Demand up to 2025: Shows the projected demand for groundwater in the future.
Stage of Groundwater Development: Provides the current stage of groundwater development in percentage.
The agent handles exceptions and notifies users if there is any failure in retrieving groundwater data.
Scenario: A city planner or water resource manager needs to understand the current groundwater usage in a specific district.
Solution: The user inputs the district name, and the agent provides detailed groundwater data to help in resource planning and decision-making.
Scenario: A farmer or agricultural planner wants to check the availability of groundwater for irrigation.
Solution: The user inputs their district and receives data on groundwater availability for irrigation, assisting in crop planning.
Scenario: A government official is drafting policies related to groundwater management.
Solution: The official inputs various districts and retrieves data to help understand groundwater trends and allocate resources efficiently.
Scenario: A researcher is studying groundwater depletion across various districts.
Solution: The researcher can query the agent for groundwater data across different regions for their analysis.
Agent: The agent is built using Fetch.AI's uAgents library.
Data Source: Groundwater data is stored in a JSON file and is retrieved based on district queries.
Protocol: The groundwater_protocol handles user queries and manages responses.
Data Handling: The agent processes groundwater data to provide status alerts and detailed statistics, ensuring users receive accurate updates on groundwater availability.
Historical Groundwater Data: Provide users with trends over previous years to assist in long-term planning. Predictive Analytics: Integrate machine learning models to predict future groundwater levels based on current usage. Web Interface: Develop a web interface where users can visually explore groundwater data. Localization: Support for multiple languages to cater to a global audience.
This project is created with support of Fetch.ai Innovation Lab, Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India.