Advances in Hydrologic Forecasts and Water Resources Management
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.
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| Format: | Book Chapter |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | DOAB: description of the publication |
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| LEADER | 04074naaaa2201117uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_76640 | ||
| 005 | 20220111 | ||
| 020 | |a books978-3-0365-1679-0 | ||
| 020 | |a 9783036516806 | ||
| 020 | |a 9783036516790 | ||
| 024 | 7 | |a 10.3390/books978-3-0365-1679-0 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 072 | 7 | |a GP |2 bicssc | |
| 100 | 1 | |a Chang, Fi-John |4 edt | |
| 700 | 1 | |a Guo, Shenglian |4 edt | |
| 700 | 1 | |a Chang, Fi-John |4 oth | |
| 700 | 1 | |a Guo, Shenglian |4 oth | |
| 245 | 1 | 0 | |a Advances in Hydrologic Forecasts and Water Resources Management |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (109 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a All rights reserved |4 http://oapen.org/content/about-rights | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 653 | |a water resources management | ||
| 653 | |a landslide | ||
| 653 | |a dammed lake | ||
| 653 | |a flood risk | ||
| 653 | |a time-varying parameter | ||
| 653 | |a GR4J model | ||
| 653 | |a changing environments | ||
| 653 | |a temporal transferability | ||
| 653 | |a western China | ||
| 653 | |a cascade hydropower reservoirs | ||
| 653 | |a multi-objective optimization | ||
| 653 | |a TOPSIS | ||
| 653 | |a gravitational search algorithm | ||
| 653 | |a opposition learning | ||
| 653 | |a partial mutation | ||
| 653 | |a elastic-ball modification | ||
| 653 | |a Snowmelt Runoff Model | ||
| 653 | |a parameter uncertainty | ||
| 653 | |a data-scarce deglaciating river basin | ||
| 653 | |a climate change impacts | ||
| 653 | |a generalized likelihood uncertainty estimation | ||
| 653 | |a Yangtze River | ||
| 653 | |a cascade reservoirs | ||
| 653 | |a impoundment operation | ||
| 653 | |a GloFAS-Seasonal | ||
| 653 | |a forecast evaluation | ||
| 653 | |a small and medium-scale rivers | ||
| 653 | |a highly urbanized area | ||
| 653 | |a flood control | ||
| 653 | |a whole region perspective | ||
| 653 | |a coupled models | ||
| 653 | |a flood-risk map | ||
| 653 | |a hydrodynamic modelling | ||
| 653 | |a Sequential Gaussian Simulation | ||
| 653 | |a urban stormwater | ||
| 653 | |a probabilistic forecast | ||
| 653 | |a Unscented Kalman Filter | ||
| 653 | |a artificial neural networks | ||
| 653 | |a Three Gorges Reservoir | ||
| 653 | |a Mahalanobis-Taguchi System | ||
| 653 | |a grey entropy method | ||
| 653 | |a signal-to-noise ratio | ||
| 653 | |a degree of balance and approach | ||
| 653 | |a interval number | ||
| 653 | |a multi-objective optimal operation model | ||
| 653 | |a feasible search space | ||
| 653 | |a Pareto-front optimal solution set | ||
| 653 | |a loss-benefit ratio of ecology and power generation | ||
| 653 | |a elasticity coefficient | ||
| 653 | |a empirical mode decomposition | ||
| 653 | |a Hushan reservoir | ||
| 653 | |a data synthesis | ||
| 653 | |a urban hydrological model | ||
| 653 | |a Generalized Likelihood Uncertainty Estimation (GLUE) | ||
| 653 | |a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) | ||
| 653 | |a uncertainty analysis | ||
| 653 | |a NDVI | ||
| 653 | |a Yarlung Zangbo River | ||
| 653 | |a machine learning model | ||
| 653 | |a random forest | ||
| 653 | |a Internet of Things (IoT) | ||
| 653 | |a regional flood inundation depth | ||
| 653 | |a recurrent nonlinear autoregressive with exogenous inputs (RNARX) | ||
| 653 | |a artificial intelligence | ||
| 653 | |a machine learning | ||
| 653 | |a multi-objective reservoir operation | ||
| 653 | |a hydrologic forecasting | ||
| 653 | |a uncertainty | ||
| 653 | |a risk | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76640 |7 0 |z DOAB: description of the publication |