Advances in Hydrologic Forecasts and Water Resources Management (Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute, 2021)
This book collected recent studies on the latest methodological and operational advances in hydrological forecasting. Specifically, the collection of papers covers a range of topics related to improving hydrological forecasting via new datasets and innovative approaches.
English
Keywords: Research & information: general; artificial intelligence; artificial neural networks; cascade hydropower reservoirs; cascade reservoirs; changing environments; climate change impacts; coupled models; dammed lake; data synthesis; data-scarce deglaciating river basin; degree of balance and approach; elastic-ball modification; elasticity coefficient; empirical mode decomposition; feasible search space; flood control; flood risk; flood-risk map; forecast evaluation; generalized likelihood uncertainty estimation; Generalized Likelihood Uncertainty Estimation (GLUE); GloFAS-Seasonal; GR4J model; gravitational search algorithm; grey entropy method; highly urbanized area; Hushan reservoir; hydrodynamic modelling; hydrologic forecasting; impoundment operation; Internet of Things (IoT); interval number; landslide; loss-benefit ratio of ecology and power generation; machine learning; machine learning model; Mahalanobis-Taguchi System; multi-objective optimal operation model; multi-objective optimization; multi-objective reservoir operation; NDVI; opposition learning; parameter uncertainty; Pareto-front optimal solution set; partial mutation; probabilistic forecast; random forest; recurrent nonlinear autoregressive with exogenous inputs (RNARX); regional flood inundation depth; risk; Sequential Gaussian Simulation; signal-to-noise ratio; small and medium-scale rivers; Snowmelt Runoff Model; Technique for Order Preference by Similarity to Ideal Solution (TOPSIS); temporal transferability; Three Gorges Reservoir; time-varying parameter; TOPSIS