Dear Colleagues,

In the last two decades, developments in computational intelligence and machine learning greatly expanded the capabilities of empirical modeling in hydrological applications via data-driven modeling. Examples include artificial neural networks, fuzzy rule-based models, and population-based techniques. Data-driven modeling techniques are generally based on analyzing field data, using a training data set that is representative of the system behavior, and determining the relationship between the system state variables (input, internal and output variables), without including prior explicit knowledge of the physical behavior of the system. Additionally, computational intelligence and machine learning techniques have also been applied to perform data-mining in terms of knowledge discovery on hydrological phenomena, starting from the available measured data aimed at unveiling possible new relationships among observable variables.

More recently, enhancements in computational capabilities (e.g. parallel computing), innovations in observation and measurement devices (e.g., ICT devices), and, consequently, increased availability of hydrological data (e.g., real time data) of different time and spatial scales, open up new and fascinating scenarios for the application of data-driven modeling techniques and data-mining approaches for new knowledge discovery. The exploitation of such a great bulk of new information can contribute greatly to enhance the robustness of hydrological models for prediction of extreme phenomena (e.g. flood forecasting, rain development, etc.) mitigating the uncertainty related to ongoing climate change. Methodological innovations in this area are expected to be of direct relevance to support engineers in taking effective decisions and setting up appropriate boundary conditions for designing and managing water infrastructures.

This Special Issue aims to collate advancements in the application of data-driven modeling techniques and data-mining approaches in the different possible applications in hydrology, with the purpose of sharing opinions, encouraging the exchange of information and knowledge acquired, promoting the integration of technological progress and scientific knowledge, and helping to increase the acceptance of such applications in the international scientific community.

Due to the interdisciplinary nature of the data-driven modeling techniques and data-mining approaches, there are no preferential topics in the field of hydrology, also because it is in the spirit of this special issue to promote the exchange of experiences and knowledge even from apparently antipodal applications.

Dr. Luigi Berardi
Dr. Daniele Laucelli
Guest Editors

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• data-driven modeling
• data mining
• computational intelligence
• machine learning
• hydrological cycle
• rainfall-runoff
• groundwater
• hydrological modeling
• climate change
• in-situ monitoring
• real-time monitoring