Development of advanced groundwater recharge estimation techniques using remote sensing and AI in the Bilate catchment, Ethiopia

Details

PROJECT SUMMARY

Project name

Development of advanced groundwater recharge estimation techniques using remote sensing and AI in the Bilate catchment, Ethiopia

Project short name

RECHARGE-AI Bilate

Project phase

I

Partner(s)/ country(ies)

Ethiopian Water Technology Institute (EWTI)

Project ID

Res/ AWTI /079/26

Project type

Research

Project implementation location

Bilate River Catchment, Southern Ethiopia (South-Central Rift Valley region, Ethiopia).

Target communities

Communities living in the Bilate River Catchment, Southern Ethiopia, who depend on groundwater from wells and boreholes for domestic water use, small-scale irrigation, and livestock production.

AMU-Project Coordinator

Mullusew Bezabih

Partner-Coordinator

Zemenu Addis

AMU-Principal

Mullusew Bezabih

AMU-Co-Investigators

Dr Sintayehu Yadete, Meron Mohammed, Getachew Enssa, Demiso Daba, Babur Tesfesa, Sufiyan Abdurhman, Dr Aschalewu Cherie, Tafese Fitensa, Kinfe Bereda, Zelalem Anley

Total budget (ETB)

940,400.00

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

Mullusew Bezabih: This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.

PROJECT DESCRIPTION

This study aims to develop advanced methods for estimating groundwater recharge in the Bilate River Catchment, Southern Ethiopia, using remote sensing data and artificial intelligence techniques. Groundwater recharge is a key component of the hydrological cycle, but it is often difficult to estimate accurately due to limited field data and complex environmental conditions. This research will integrate satellite-based environmental data with AI-based modeling approaches to improve the estimation of spatial and temporal patterns of groundwater recharge. The expected outcome is a more reliable and scalable approach for recharge estimation in data-scarce catchments, which can support sustainable groundwater management and water resource planning in the region.