The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

AI and Satellite Imagery in the Service of Sustainable Management of Groundwater Resources

Funding agency: SRA-MECW. Duration: 2019-2021

The Middle East faces various environmental challenges, including water scarcity. Climate change makes surface waters less reliable and consequently adds up the pressure on groundwater resources, which are already the main supply for agriculture, industry, and drinking sectors. Overexploitation of groundwater leads to the depletion of groundwater resources, possible permanent loss of aquifer storage, land subsidence, and many socio-economic challenges.

The project's expected outcomes will help the stakeholders, and water resources managers clearly understand and evaluate their water-related and land use planning strategies. Apart from the technical parts, this project will also provide some interdisciplinary solutions considering the economic, social, and water-related dimensions to improve groundwater resources.

Groundwater data in the Middle East are not monitored very well, and therefore, there is a lack of data on these essential resources. This project implements various remote sensing-based factors, and artificial intelligence algorithms in order to generate a better understanding of groundwater resources in this area. This can benefit the Middle Eastern countries to manage groundwater resources more efficiently and sustainably.


Objectives of the project:

  • Using remote sensing-derived factors and machine learning algorithms for modeling groundwater potential.
  • Developing a new framework for analyzing groundwater resources at data-scarce regions.
  • Developing a novel python-based ArcGIS toolbox to improve the performance of the existing machine learning algorithms in modeling groundwater resources.
  • Using artificial intelligence and remote sensing to determine the long-term variations of ground deformation and extract the meaningful relationships between pumping-induced land subsidence and its driving factors.


Kamali Maskooni, E., Naghibi, S.A., Hashemi, H. and Berndtsson, R., 2020. Application of advanced machine learning algorithms to assess groundwater potential using remote sensing-derived dataRemote Sensing12(17), p.2742.

Naghibi, S.A., Hashemi, H. and Pradhan, B., 2021. APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies. Geoscience Frontiers12(6), p.101232.

Naghibi, S.A., Hashemi, H., Berndtsson, R. and Lee, S., 2020. Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors. Journal of Hydrology589, p.125197.

Naghibi, S. A., Hashemi, H., & Khodaei, B. (2022). An Integrated InSAR-Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas. Journal of Hydrology, 127627.

Research Team

Seyed Amir Naghibi, CMES Researcher and Postdoctoral Fellow at the Division of Water Resources Engineering (Department of Building and Environmental Technology, Lund University) 

seyed_amir [dot] naghibi [at] tvrl [dot] lth [dot] se

Hossein Hashemi, CMES Researcher and Associate Professor at the Division of Water Resources Engineering (Department of Building and Environmental Technology, Lund University) 

hossein [dot] hashemi [at] tvrl [dot] lth [dot] se