Advances in Streamflow and Flood Forecasting

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

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