Comparative Water Resources Availability in the Middle East
Funding agency: SRA-MECW. Duration: 2021-ongoing
The Middle East (ME) is an arid area with low precipitation and high evapotranspiration in most seasons of the year. Several natural factors and non-natural human activities have put the groundwater and surface water resources of the ME in risk. In this project, advanced remote sensing (RS), machine learning (ML), and artificial intelligence (AI) methods are being utilized to assess the availability of water resources in the ME region.
Climate change, improper management of natural resources and environmental activities, high rate of population growth in metropolitan areas, and increasing agricultural activities are huge risks for groundwater and surface water resources the ME region. In this PhD research project RS will be used to evaluate the water resources in the ME, due to general lack of rich ground-based methods.
Interferometric Synthetic Aperture Radar (InSAR) is one of the most powerful tools to evaluate the groundwater resources based on satellite image processing techniques and it has been chosen as the main core of this research. In this project the potential of InSAR in combination with cutting-edge ML and AI methods will be investigated. The output of this research will be valuable for determining the workarounds to tackle the increasing shortage of water resources in ME region.
Objectives
This PhD project will address the following research questions:
- How remote sensing data could be used to evaluate the current and future conditions of water resources in the ME region?
- Considering the lack of ground-based data in the ME region, how can we use satellite based InSAR methods to build a model for the trend of ground water resources?
- How the ML/AI algorithms can be used to improve the existing RS-based methods and possible develop new methods for management of water resources in the ME area?
Research Activities
- Paper presentation by Behshid Khodaei at the European Geosciences Union's General Assembly in Vienna, Austria, 23-28 April 2023: "InSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions" (authored by Behshid Khodaei, Hossein Hashemi, Amir Naghibi and Ronny Berndtsson)
Research Team
Behshid Khodaei, Doctoral Student at CMES and the Divison of Water Resources Engineering (Department of Building and Environmental Technology)
behshid [dot] khodaei [at] tvrl [dot] lth [dot] se (behshid[dot]khodaei[at]tvrl[dot]lth[dot]se)