PROJECT SUMMARY
|
Project name |
Ai-Driven Groundwater Monitoring Using Remote Sensing (A Case Study at: Kulfo Watershed, Ethiopia) |
|
Project short name |
AI4Groundwater |
|
Project phase |
I |
|
Partner(s)/ country(ies) |
Ethiopian Water Technology Institute (EWTI) |
|
Project ID |
Res/AWTI/078/26 |
|
Project type |
Research |
|
Project implementation location |
Kulfo Watershed, Gamo Zone, Southern Ethiopia |
|
Target communities |
Local communities within Kulfo Watershed, water resource managers, agricultural users, environmental agencies, researchers, and local government institutions involved in groundwater and watershed management. |
|
AMU-Project Coordinator |
Demiso Daba |
|
Partner-Coordinator |
Zemenu Addis |
|
AMU-Principal Investigator |
Mr. Demiso Daba |
|
AMU-Co-Investigators |
Zelalem Anley, Mullusew Bezabih, Dr. Sintayehu Yadete, Meron Mohammedamin, Getachew Enssa, Sufiyan Abdurhman, Dr. Aschalewu Cherie, Tafese Fitensa, Kinfe Bereda, Babur Tesfaye. |
|
Total budget (ETB) |
1.000,000.00 |
|
Project Period |
Start date: 5/26/2026 | end date: 5/26/2027 |
|
Project Reporting |
Quarterly |
|
Project finance management office |
College/institute finance & budget admin |
|
Contact person |
Demiso Daba: |
PROJECT DESCRIPTION
This project aims to develop an AI-driven groundwater monitoring framework for the Kulfo Watershed, Ethiopia, by integrating satellite remote sensing, optionally climate reanalysis, and ground observation data. Advanced artificial intelligence models will be used to map groundwater conditions, analyze spatial and temporal variations, identify groundwater hotspots, and detect potential recharge zones. The project will generate groundwater status maps, location-based information, and time-series analyses to support evidence-based water resource management. The resulting framework will provide a scalable and practical decision-support tool for researchers, environmental managers, and policymakers working toward sustainable groundwater management.
