Advances in Streamflow and Flood Forecasting
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 6509
Special Issue Editors
Interests: hydrological modeling; artificial intelligence; data merging; precipitation prediction; remote sensing; ensemble streamflow prediction
Special Issues, Collections and Topics in MDPI journals
Interests: hydrologic model; machine learning/deep learning; big data; water resources managements; eco-hydrology; water-food-energy nexus
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rivers and streams experience flooding as a natural result of large rainstorms or spring snowmelt that may result in inundation or flooding disasters. Flooding is considered one of the biggest weather‐related killers in the world. Precipitation intensity has increased worldwide with global climate change, but this effect on streamflow and flood magnitude is difficult to pinpoint. Therefore, more accurate streamflow and flood forecasting methods are essential for hydrologists.
This Special Issue focuses on advanced approaches including the traditional approach of statistical and stochastic time-series modeling with their recent developments, stand-alone data-driven methods such as artificial intelligence (machine learning/ deep learning), and modern hybrid approaches where data-driven models are combined with preprocessing methods (or physically based hydrologic models) to improve the accuracy of streamflow and flood forecasting.
Dr. Yen-Ming Chiang
Dr. Wen-Ping Tsai
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.
Keywords
- hydrologic modeling
- artificial intelligence
- machine learning
- deep learning
- remote sensing
- climate change
- uncertainty analysis