EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

InSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions

Behshid Khodaei1,2, Hossein Hashemi1,2, Seyed Amir Naghibi1,2, and Ronny Berndtsson1,2
Behshid Khodaei et al.
  • 1Lund University, Faculty of Engineering, Building and Environmental Technology, Lund, Sweden
  • 2Lund University, Centre for Advanced Middle Eastern Studies, Lund, Sweden

Lake Urmia, located in northwestern Iran, is the largest salt lake in the Middle East (ME) and one of the largest hypersaline lakes in the world. The lake has an important role in biodiversity preservation and the economic and cultural aspects of its surrounding region. Over the last two decades, the combined effects of climate change and anthropogenic activities have caused a significant depletion of lake water. The interaction of lake water and groundwater has motivated us to study the surrounding aquifers to determine the impact of human activities on the lake. The Shabestar plain located in the northeast of Lake Urmia is chosen as the research area for the current study. The goal is to find a Remote Sensing (RS) based method to estimate the changes in groundwater level, due to over-exploitation, both in time and space. We use a random forest algorithm to determine the contribution of different factors in the estimation of the aquifer’s hydraulic properties. Input data include the surface deformation rate produced by Interferometric Synthetic Aperture Radar (InSAR) technique between 2016 and 2022, weather-driven parameters including temperature, precipitation, soil moisture, normalized differential vegetation index, and evapotranspiration, and the hydrological factors including observed well and lake water levels. The built model is then used for estimating the spatiotemporal groundwater level changes throughout the aquifer. The groundwater level change and its relationship with the lake water surface is investigated. The model has the potential to be generalized in the estimation of groundwater depletion in similar aquifers.

How to cite: Khodaei, B., Hashemi, H., Naghibi, S. A., and Berndtsson, R.: InSAR-AI-Based Approach for Groundwater Level Prediction in Arid Regions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-754,, 2023.