Data mining (DM) methods have accomplished excellent achievements in civil engineering, especially for water-related engineering issues in recent years. These approaches have proven their applicability and potential to model and simulate hydraulic engineering parameters and water resources, becoming hot topics in water-related problems.

DM methods are composed of many algorithms with unique features. These algorithms include fuzzy logic, artificial neural networks, decision trees algorithms, evolutionary polynomial regression, variant types of genetic programming, support vector machines, etc. Due to the variety and high ability of these techniques to simulate complex systems, they have become very intriguing in engineering. In addition, the promising results obtained from DM approaches have led to the increased popularity and use of these approaches in real-world applications. Hence, DM applications are exciting and widespread research topics in water-related problems.

On the other hand, DM models are closely related to evolutionary algorithms and optimization methods. The optimization algorithms can usually enhance the efficiency of DM techniques, in terms of accuracy and cost-effectiveness.

DM methods are used for knowledge discovery and provide innovative applications for modeling engineering problems. Since water engineering deals with a large amount of data, we need powerful tools to provide valuable and usable knowledge for engineers. DM methods work based on data, extracting interesting patterns from it. Its knowledge discovery has become one of the hottest and most interesting topics in water engineering. These facts highlight the necessity of DM tools as robust methods for extracting practical knowledge from data. Therefore, the applications of these algorithms are continuously increasing.

This Special Issue welcomes the application of DM, new advancements, and developments for solving water-related engineering problems, including hydrology, hydraulic and environmental engineering problems. This Special Issue invites theoretical and practical research of DM approaches in water-related cases. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Applications of DM for water and wastewaters
• Applications of DM in hydrology
• DM methods in hydraulic engineering, river engineering, environmental engineering, and water resource management
• Hybrid DM methods for solving water-related problems
• Hybrid DM methods with evolutionary algorithms
• DM applications in climate change
• DM applications in large-scale hydropower project constructions
• DM applications for water supply and water demand
• DM applications in water quality modeling
• DM for streamflow forecasting
• DM for rainfall prediction
• DM for the operation of reservoir and flood control
• DM for groundwater modeling
• DM for management and operation of hydraulic structures
• DM for prediction of sediment transport in river and reservoir