Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems

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

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