Machine Learning Applications in Hydrology: Current Trends and Future Challenges
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3158
Special Issue Editors
Interests: climate change; ecohydrology; wetlands; artificial intelligence; remote sensing; model-data fusion
Special Issue Information
Dear Colleagues,
Complex hydrological systems are characterized by processes and events whose dynamics depend on various direct and indirect factors. It has been argued that large-scale hydrological data sets may include substantially more information than hydrologists have been able to translate into theory or process-based models. Due to the enormous advances in computational power, machine-learning algorithms have recently undergone substantial advancements in handling and processing complex and big data, which has sparked a surge in machine learning applications across all domains of hydrology.
In this Special Issue, we invite researchers to contribute papers on the application of machine-learning methods together with big data to address critical issues in hydrology. The final results should either enhance our understanding of fundamental hydrological processes or provide a novel perspective on the current trends and future challenges in applying machine learning techniques to hydrology. We encourage papers on combining physics-based modelling of hydrological systems and machine learning. In addition, we welcome authors to disclose any data and code specific to their analyses that can be distributed legally. Review papers will also be considered.
Potential topics for this Special Issue may include, but are not limited to, the following:
- Integrating scientific knowledge with machine learning for hydrological systems;
- Improving interpretability in machine learning approaches;
- Uncertainty quantification, propagation, and characterization in model networks;
- Developing benchmark data sets for machine learning applications in hydrology;
- Assembling multiple physics-based models via machine learning methods.
Dr. Hao Chen
Dr. Ning Nie
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.
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.
Keywords
- machine learning
- big data
- hydrology
- water cycle
- hydrologic scaling, similarity, and heterogeneity
- model–data fusion
- interpretable artificial intelligence
