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Advances in Hydrologic Forecasts and Water Resources Management

The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters...

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Detalhes bibliográficos
Corporate Authors:	Chang, Fi-John, Guo, Shenglian
Formato:	Livro
Idioma:	Inglês
Publicado em:	MDPI - Multidisciplinary Digital Publishing Institute 2020
Assuntos:	thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and 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
Acesso em linha:	https://directory.doabooks.org/handle/20.500.12854/68957
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Advances in Hydrologic Forecasts and Water Resources Management

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