Special Issue "Application of Machine Learning and Artificial Intelligence in Surface Flow Simulation"
Deadline for manuscript submissions: closed (31 July 2020).
Interests: uncertainty analysis; extreme hydrological events and climate change; model-data analysis in water resources systems
Interests: modelling of environmental hydraulics; vegetation hydrodynamics and the the interaction of vegetation and flow in river and estuarine systems; 3-D Computational Fluid Dynamics modelling of environmental hydraulics; the impact of climate change on ecosystems and the linkage between hydrodynamics and invertebrate habitat in rivers
Interests: Hydroinformatics; Environmental Information Systems Scientific Visualization Cyber Systems Design and Development Machine Intelligence; Data Analytics; Information and Communication Technologies
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 surface flow simulation in the past few years. AI and its subfields including machine learning (ML) and deep learning (DL) have proved to be proficient for predictive modeling and exploratory data analysis, particularly in drainage systems with complex and non-linear processes. This Special Issue of Applied Sciences welcomes theoretical and AI/ML/DL computational modeling including data-driven and model-driven contributions that can advance our understanding of surface flow processes and predictability. In particular, (i) approaches that can aggregate a wide variety of data sources in simulation, including machine learning and deep learning based simulation techniques, (ii) computing systems with sophisticated optimization techniques that can quantify—and ideally minimize—the error and uncertainty associated with models and data that is used to understand and initialize watershed-scale models, (iii) computational learning techniques for the flow simulation of non-linear and complex systems, and (iv) data modeling and database development for structured and non-structured river flow data design. We also encourage contributions in integrative approaches such as physical based surface flow modeling and AI/ML/DL computational systems, especially at local, national, and global scales.Dr. Vidya Samadi
Dr. Catherine Wilson
Dr. Ibrahim Demir
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence
- machine learning and deep learning approaches
- surface flow simulation
- uncertainty assessment
- integrative modeling systems
- database development and design