Remote Sensing - AI in Hydrology
Water is at the core of many of today’s most pressing environmental and societal challenges, from managing floods and droughts to securing sustainable agricultural production under a changing climate. Reliable observation, monitoring, and prediction of hydrological processes are therefore critical for ensuring water security and resilience of societies. In recent years, the rapid growth of satellite remote sensing technologies and advances in Artificial Intelligence (AI)—especially machine learning and deep learning—have opened transformative opportunities for hydrological sciences.
The Remote Sensing–AI in Hydrology research group uses satellite and in-situ observations together with advanced AI-based modelling frameworks to understand, monitor, and predict hydrological variables and water resources from local to global scales. Our work bridges fundamental science with practical applications, ranging from precipitation estimation and groundwater monitoring to natural hazard assessment and precision agriculture.
We also develop advanced AI-driven decision support and policy modelling systems to address complex challenges in water resources, agriculture, climate adaptation, and natural hazard management to facilitate decision-making procedure with a transboundary, international, and systematic mindset at national, continental, and global scales.
Areas
- Data sources: Satellite precipitation (TRMM-Tropical Rainfall Measuring Mission and GPM-Global Precipitation Measurement), land surface deformation and groundwater (InSAR-Interferometric Synthetic Aperture Radar), and weather radar (X-band and Micro Rain Radar-MRR).
- Techniques: Machine learning, deep learning, data-driven approaches, and AI-based decision support systems.
- Applications: Monitoring and modeling hydrological processes and natural hazards (floods, dust storms, landslides), water resources assessment, and precision agriculture under climate change.
Contact
Associate Prof. Hossein Hashemi
hossein [dot] hashemi [at] tvrl [dot] lth [dot] se (hossein[dot]hashemi[at]tvrl[dot]lth[dot]se)
Assistant Prof. Amir Naghibi
Amir [dot] naghibi [at] tvrl [dot] lth [dot] se (amir[dot]naghibi[at]tvrl[dot]lth[dot]se)
Involved researchers
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Funding partners
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