Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources

Dear Colleagues,

In the face of climate change and population growth in many parts of the world, we need appropriate tools that can assist in dealing with the difficulties introduced by the increasing complexity of water problems. Flooding and drought hazards cause numerous economic and life losses in the present changing climate and environment. It is, therefore, important to continue developing and improving our knowledge in the field of flood vulnerability assessment and hazard alleviation. Water resource management at the catchment level is a scientific discipline with great environmental importance. It is a multidisciplinary issue, which has prevailed from the cooperation of a wide range of scientists, such as engineers, Earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all the users’ needs in domestic, agricultural, industrial, and ecological uses are satisfied with the least controversy and conflict. In order to achieve operational and efficient water management, we need to have reliable methodologies. This Special Issue will feature the latest advances and developments in operational hydrologic forecasts and water resource management. The focus is centered on advanced machine learning and deep learning methods for operational hydrologic forecasting for optimal water resource management. The computational power available today allows us to tackle simulation challenges in hydraulic and hydrological modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, design or optimization of hydraulic structures, calibration of model parameters, uncertainty quantification, real-time model-based control, etc.

To address these issues, the development of fast computing models to increase the simulation speed seems to be a promising strategy: It does not require a huge investment in new hardware and software, and the same tools can be used to solve very different problems. The main themes of this Special Issue include but are not limited to the following: 

• Application of advanced machine learning models including deep learning methods for precise hydrologic forecasting (modeling rainfall, runoff, sediment, surface water and groundwater quality, lake level, water temperature, reservoir inflow, evaporation, evapotranspiration etc.);
• Utilization of advanced machine learning models with ensemble models for solving water resource problems;
• Spatial and temporal modeling of hydrological variable with aid of advanced computing models;
• Coupling of data preprocessing techniques with machine learning methods to capture noise and nonlinear of hydrological variables;
• Use and development of novel metaheuristic algorithms with machine learning methods to enhance their computing abilities.

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

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• metaheuristic algorithms
• data mining and deep learning
• prediction
• modeling
• optimization
• hybridization
• soft computing
• streamflow
• rainfall–runoff
• evaporation, evapotranspiration
• water resource management
• conservation and sustainability