Advances in Hydrologic Forecasts and Water Resources Management

• Other Authors: Chang, Fi-John (Editor), Guo, Shenglian (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: Research & information: general; water resources management; landslide; dammed lake; flood risk; time-varying parameter; GR4J model; changing environments; temporal transferability; western China; cascade hydropower reservoirs; multi-objective optimization; TOPSIS; gravitational search algorithm; opposition learning; partial mutation; elastic-ball modification; Snowmelt Runoff Model; parameter uncertainty; data-scarce deglaciating river basin; climate change impacts; generalized likelihood uncertainty estimation; Yangtze River; cascade reservoirs; impoundment operation; GloFAS-Seasonal; forecast evaluation; small and medium-scale rivers; highly urbanized area; flood control; whole region perspective; coupled models; flood-risk map; hydrodynamic modelling; Sequential Gaussian Simulation; urban stormwater; probabilistic forecast; Unscented Kalman Filter; artificial neural networks; Three Gorges Reservoir; Mahalanobis-Taguchi System; grey entropy method; signal-to-noise ratio; degree of balance and approach; interval number; multi-objective optimal operation model; feasible search space; Pareto-front optimal solution set; loss-benefit ratio of ecology and power generation; elasticity coefficient; empirical mode decomposition; Hushan reservoir; data synthesis; urban hydrological model; Generalized Likelihood Uncertainty Estimation (GLUE); Technique for Order Preference by Similarity to Ideal Solution (TOPSIS); uncertainty analysis; NDVI; Yarlung Zangbo River; machine learning model; random forest; Internet of Things (IoT); regional flood inundation depth; recurrent nonlinear autoregressive with exogenous inputs (RNARX); artificial intelligence; machine learning; multi-objective reservoir operation; hydrologic forecasting; uncertainty; risk
• Online Access: DOAB: download the publication; DOAB: description of the publication