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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Bibliographic Details
Other Authors: Chang, Fi-John (Editor), Guo, Shenglian (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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Online Access:DOAB: description of the publication
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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 
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