Hydrological Simulation and Forecasting Based on Artificial Intelligence
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 20 August 2024 | Viewed by 5293
Special Issue Editor
Interests: hydrological–hydrodynamic modelling; groundwater modelling; parallel computing; artificial intelligence; remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the development of modern artificial intelligence and parallel computing, the applications of these novel technologies in the field of hydrological and hydrodynamic modeling, flood simulation and forecasting, risk and uncertainty analysis, etc., have significantly improved accuracy, reliability, and computational efficiency in the domain of disaster defense. This Special Issue mainly focuses on the application and novel methods of flood simulation, forecasting and modeling, model parameter estimation, risk and uncertainty analysis, and data analysis, based on modern artificial intelligence and/or parallel computing technologies. We invite submissions including, but not limited to, the following topics:
(1) Artificial-intelligence-aided hydrological simulation and forecasting.
(2) Hydrodynamic modelling based on artificial intelligence technologies.
(3) Flood risk analysis, hydrological or hydrodynamic model uncertainty analysis based on intelligence optimization algorithms or other related artificial intelligence techniques.
(4) Model parameter optimization algorithms based on intelligence optimization algorithms.
(5) Data analysis based on artificial intelligence or machine leaning technologies.
(6) Hydrological/hydrodynamic modeling, risk and uncertainty analysis, parameter optimization, data analysis, etc., accelerated by modern parallel computing technologies such as many-core GPU, multi-core CPU, or large-scale parallel computer clusters.
Dr. Guangyuan Kan
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- parallel computing
- GPU computing
- hydrological model
- hydrodynamic model
- flood simulation
- flood forecasting
- risk and uncertainty analysis
- data analysis