AI Solutions for Improving Sustainability in Water Resource Management

Dear Colleagues,

Water resources play a pivotal role in fostering sustainable socio-economic advancement and preserving the environment for future generations. While prevalent techniques in water resource management primarily hinge on time series modeling, they often presume linearity in water demand and usage data. These conventional approaches employ models and methods that overlook the intricacies inherent in the datasets. Hence, the precision of forecasting water quantity and quality time series holds immense significance for sustainable progress, impacting economic, social, and environmental domains.

The examination of historical datasets through cutting-edge artificial intelligence modeling techniques is a promising avenue for innovative water resources management solutions. This field holds the potential to surmount the limitations posed by complex input datasets inherent in deterministic hydrologic models. This Special Issue endeavors to address two core objectives:

1. The development of novel pioneering artificial intelligence (AI) and stochastic techniques tailored for modeling water quantity and quality time series, which could overcome the limits of conventional methodologies;
2. The establishment of more accurate and streamlined predictive models, geared towards real-time forecasting, optimization, and the automation of meteorological and hydrological watershed variables. These efforts are directed to enhance our comprehension of water resource management challenges entwined with the realm of sustainable development in today's swiftly globalizing and urbanizing landscape.

Within this context, research that delves into the intricate and dynamic meteorological and hydrological watershed variables, coupled with the integration of novel modeling approaches, tool creation, and enhancements in existing predictive models, is of utter significance. Thus, this Special Issue seeks to provide a platform for the exchange of knowledge and expertise in the sphere of water sustainable water resource management.

We look forward to receiving your contributions.

Dr. Hossein Bonakdari
Prof. Dr. Bahram Gharabaghi
Dr. Silvio José Gumiere
Guest Editors

Manuscript Submission Information

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• time series
• watershed
• artificial intelligence
• stochastic methods
• hydrology
• sustainability
• hydrological processes
• real-time prediction
• optimization algorithms
• predictive modelling
• water balance
• environmental sustainability
• water demand
• meteorological variables
• water quantity and quality
• watershed variables