Advances in Hydrologic Forecasts and Water Resources Management

• Autore: Chang Fi-John
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (272 p.)
• Soggetto topico: Research and information: general
• Soggetto non controllato: 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; uncertainty; uncertainty analysis; Unscented Kalman Filter; urban hydrological model; urban stormwater; water resources management; western China; whole region perspective; Yangtze River; Yarlung Zangbo River
• Persona (resp. second.): GuoShenglian; ChangFi-John
• Sommario/riassunto: 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. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
• Titolo autorizzato: Advances in Hydrologic Forecasts and Water Resources Management
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557110703321
• Lo trovi qui: Univ. Federico II