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.

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• artificial intelligence
• deep learning

• River System Modeling

• uncertainty assessment

• Hybrid Modeling Systems

• database development and design