Integrating Artificial Intelligence in Hydrology, Hydrodynamics and Water Quality for Sustainable Water Management
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 32
Special Issue Editors
Interests: water and water quality management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Traditional quantitative analysis methods of hydrological, hydrodynamic, and water quality processes are based on research from the 19th and 20th centuries. Most computational tools based on those methods were developed during the 20th century when climate variability was not an issue. Climate change (CC) effects in the hydrological cycle, hydrodynamic regimes, and water quality processes pose a significant challenge to water management. Deterministic and statistical methods of analysis that rely on historical data may not generate realistic water management scenarios if the severity, duration, and frequency of extreme events change over time. Water management measures designed using traditional methods may not be applicable due to CC-induced changes in precipitation, tidal, temperature, and wind regimes. New water management approaches are needed to capture the changing dynamics of the environmental variables so that sustainable management measures are generated.
Based on the above premises, this Special Issue aims to explore the integration of Artificial Intelligence (AI) in hydrological, hydrodynamics, and water quality modelling for Sustainable Water Management.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
Hindcasting or forecasting environmental time series (pollutants, precipitation, groundwater recharge, salinity, tides, wind, etc.) using neural networks under changing climate.
Assessment of regulatory water quality parameters compliance in rivers and estuaries using fuzzy sets to account for climate change and sea level rise.
In water quality and salinity modelling, a water body is discretized in spatial cells or elements to estimate the concentrations of a particular pollutant in each cell or element. The state of neighbouring cells/elements determines the state of each cell or element. Cellular automata algorithms also postulate that the states of the adjacent cells can determine each cell’s state and that their evolution over time determines the entire system's behavior. Cellular automata use simple transition rules to simulate local–global dynamics and may be better suited to capture changes in water bodies' boundaries due to climate change. Contributions in this area of research are also welcomed.
We look forward to receiving your contributions.
Dr. Vladimir J. Alarcon
Dr. Paul Mickle
Guest Editors
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Keywords
- environmental time series
- hindcasting
- forecasting
- water quality compliance
- fuzzy sets
- climate change
- sea level rise
- cellular automata
- model calibration
- genetic algorithms
- optimization
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