We are pleased to announce the opening of the Special Issue on Application of Machine Learning Algorithms in Hydrology, and we cordially invite you to submit your valuable research contributions.
Hydrology, as the science of water movement, distribution, and quality in natural systems, plays a critical role in understanding and managing water resources. Traditionally, hydrologists have relied on process-based models and empirical equations to simulate hydrological processes. However, the advent of machine learning (ML) has revolutionized the field by offering data-driven approaches that can capture complex patterns and relationships.
ML has emerged as a potent tool for analyzing complex hydrological datasets, facilitating the extraction of valuable insights and bolstering evidence-based decision-making processes. From rainfall-runoff modeling and flood prediction to groundwater assessment and water quality monitoring, machine learning is reshaping how hydrological information is processed and interpreted. This Special Issue aims to highlight the latest developments and innovative uses of machine learning techniques within the realm of hydrology.
We encourage submissions covering a wide spectrum of topics, including:
• Machine learning-based hydrological modeling and forecasting
• Integration of remote sensing data with machine learning for hydrological applications
• Data-driven approaches for water resources management and planning
• Application of deep learning techniques in hydrological data analysis and dataset gap filling
• Data-driven based Downscaling of various satellite derived hydrological variables (rainfall, soil moisture, ET)
• Hydrological data assimilation and uncertainty quantification using machine learning
• Machine learning for drought monitoring and assessment
• Optimization of water resource systems using machine learning algorithms
• Incorporation of climate change projections in hydrological modeling through machine learning
• Machine learning applications for ecohydrology and watershed management
• Physics-informed artificial Intelligence for hydrology and Land surface processes
• Ethical considerations and challenges in employing machine learning for hydrological research and applications
Both original research papers and comprehensive review articles are welcome. We also encourage submissions that present experimental studies and case studies showcasing the practical applications of machine learning in Geoinformatics.
Keywords:
- Hydrology
- Machine Learning
- Predictive Modeling
- Water Resources Managment
- Climate Change
- Remote Sensing
- Deep Learning
- Physics-informed Machine Learning
Manuscript Submission Information:
To submit to the issue, click here:
https://www.sciepublish.com/index/my/step1/journalsid/28.html
For more information on Author Instruction, please visit the following page:
https://www.sciepublish.com/journals/hee/instructions
