Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 5631
Special Issue Editors
Interests: image processing; environmental data analysis; hyperspectral imaging; object-based image analysis
Interests: urban hydrology; flood risk assessment; hydroinformatics; data-driven modelling; uncertainty analysis; stormwater management
Special Issues, Collections and Topics in MDPI journals
Interests: satellite imagery; rain radar; artificial intenlligenc; biometeorology; hydrometerology; argometerology
Interests: deep learning; ensemble learning; tensor decomposition; optimization; system identification; remote sensing
Special Issue Information
Dear Colleagues,
As natural and built systems are undergoing major challenges due to the impact of climate change and rapid urban development, new analytics and modeling solutions are required to study such spatiotemporal systems under dynamic conditions and modeling objectives. This also involves the utilization of diverse and large sources of information, amounting to massive databases or new sources of intelligence. Recent advances in artificial intelligence and computational resources facilitate innovative data-driven modeling approaches which can accommodate the changing nature of hydrological systems. The addition of remote sensing techniques enables the use of massive databases and real-time monitoring of hydrologic phenomena.
Remote Sensing is launching a special issue entitled “Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems.” This issue aims to promote state-of-the-art data-driven and machine learning techniques such as deep learning, ensemble learning, and reinforcement learning, using remote sensing in water research spanning hydro-climatology, hydroinformatics, and hydro-meteorology. Applications of interest include, but not limited to, hazard monitoring, forecasting of extreme events, pollution analysis, mapping of renewables, surface water systems, sociotechnical analysis, hydroinformatics, environment and sustainable agriculture applications. Research featuring advances in statistical modeling approaches is also invited. Consideration will be also given to interdisciplinary methodologies in uncertainty analysis, state-estimation, model interpretability, system identification and relational mapping of remotely sensed systems.
Dr. Prashanth Reddy Marpu
Dr. Usman T. Khan
Dr. Ju-Young Shin
Dr. Mohammad H. Alobaidi
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- big data analytics
- satellite
- Radar
- data assimilation
- spatiotemporal modeling
- hydrology
- hydroinformatics
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