Prediction and Assessment of Hydrological Processes

Dear Colleagues,

This Special Issue will feature the latest advances and developments in sustainable hydrological cycling. Simulated hydrological responses of river basins remain highly uncertain, due to the presence of a broad variety of schematizations, erroneous measurements, and prior assumptions. Accurate and reliable runoff predictions made using the rainfall–runoff models should be a core component for flood risk management. However, since most areas around the world remain ungauged, identifying the parameters of rainfall–runoff models is still a challenge that may lead to the use of advance computational methods to overcome uncertainty in runoff predictions. In addition, in water resource management at present, there are different challenges and uncertainties caused by climate change and manmade interferences, so it can be very difficult to make decisions regarding this problem. Further, the mismanagement and sustainability of current and future water resource allocation methods are also concerns. Thus, it is important to use the newest technology and tools to improve and effectively develop sustainable management methods.

The main themes of this Special Issue include, but are not limited to, the following:

• The use of advanced computing methods for precise hydrological variable forecasting (modeling streamflow, floods, sediment, air temperature, evaporation, evapotranspiration, etc.);
• The utilization of advanced machine learning and deep learning models with ensemble models for solving hydrological problems;
• The spatial and temporal modeling of hydrological variables with the aid of advanced computing models;
• The coupling of data preprocessing techniques with machine learning and deep learning methods to capture noise and nonlinear hydrological variables;
• The use and development of novel optimization algorithms with machine learning methods to enhance their computing abilities.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Dr. Mo Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• hydrological modeling
• water resource management
• machine learning
• deep learning