Application of Artificial Intelligence for River Hydrodynamics Modeling
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 2475
Special Issue Editors
Interests: uncertainty analysis; extreme hydrological events and climate change; model-data analysis in water resources systems
Interests: environmental hydraulics; flooding; fish kinematics; hydrokinetic turbines
Interests: hydroinformatics; intelligent systems; scientific computing; scientific visualization; data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The development of increasingly sophisticated artificial intelligence (AI) techniques, combined with rapid increases in computing power, has prompted research into advanced methods for data-driven and model-driven river system simulation in the past few years. AI techniques and its subfields including machine learning have proved to be proficient for predictive modeling and exploratory data analysis, particularly in river systems with exhibit complex and non-linear processes. This Special Issue of Applied Sciences welcomes computational modeling and AI-driven approaches for river engineering problems including river hydraulic modeling, hydrological simulation, hybrid simulation of hydraulics and machine learning, and data fusion and predictability. In particular (i) approaches that can aggregate a wide variety of data sources in simulation, including deep learning based river system simulation techniques, (ii) computing systems with advanced optimization techniques that can quantify, and ideally minimize the error and uncertainty associated with models and data integration, (iii) computational learning techniques for river dynamic computation of non-linear and complex systems, and (iv) data modeling and database development. We also encourage contributions in integrative approaches such as integrating AI with traditional 2D/3D river computational modeling, physics-based streamflow simulation, and water resources related data mining and computational systems especially at local, national, and continental scales.
Dr. Vidya SamadiDr. Catherine Wilson
Dr. Ibrahim Demir
Guest Editors
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Keywords
- artificial intelligence
- deep learning
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River System Modeling
- uncertainty assessment
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Hybrid Modeling Systems
- database development and design
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