Applied Machine Learning for River Components Studies

Applied Machine Learning for River Components Studies is a burgeoning field that addresses the critical need for advanced data analysis in the context of river systems. Rivers are vital for surface water supplies and significantly impact anthropogenic, environmental, ecological, and natural resource activities. The increasing global population has intensified industrial and agricultural activities, leading to significant stress on river systems and resulting in quantitative and qualitative issues. River components encompass a wide array of factors, including discharge, flood, baseflow, anthropogenic activities, environmental flow and demand, sediment load, groundwater interaction, water quality, ecological characteristics, erosion, sedimentation, and the impacts of dams and reservoirs. Recent advancements in sensor technology, remote sensing, and the Internet of Things (IoT) have facilitated the collection of extensive and diverse datasets related to these components. However, the complexity and volume of this data necessitate sophisticated data handling and modeling techniques. Despite the progress, there remains a gap in effectively utilizing this data to address the multifaceted challenges associated with river components.

This research topic aims to explore the application of machine learning and deep learning techniques to enhance the understanding and management of river components. The primary objectives include reducing uncertainty, minimizing computational time, increasing accuracy, precision, and reliability, and enhancing the generalization of models. Specific questions to be addressed include: How can machine learning models improve the prediction and management of river discharge and flood events? What role can reinforcement learning play in optimizing river component simulations? How can data pre-processing and post-processing techniques be refined to improve model outcomes? By answering these questions, the research aims to provide robust, data-driven solutions to the complex problems facing river systems.

To gather further insights into the application of machine learning in river component studies, we welcome articles addressing, but not limited to, the following themes:
- Physics-informed neural networks in river components interpretation
- Monitoring and supervising issue-related big data in river components using deep learning techniques
- Reinforcement learning techniques as alternatives to conventional methods for modeling river components data
- Evaluating the impact of data pre-processing and post-processing on river components simulations
- Development of novel and hybrid machine learning models for river component studies
- Modeling high-dimensional data for river components

This research topic seeks to bridge the gap between advanced machine learning techniques and the complex, data-rich field of river component studies, fostering interdisciplinary collaboration and innovation."

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Technology and Code

Keywords: Supervised learning, Unsupervised learning, Prediction, Forecasting, Classification, Clustering, Regression, IoT, Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.