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Advances in Hydrologic Forecasts and Water Resources Management
Advances in Hydrologic Forecasts and Water Resources Management
Autore Chang Fi-John
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (272 p.)
Soggetto topico Research & information: general
Soggetto non controllato 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
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557110703321
Chang Fi-John  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Intelligence Techniques in Hydrology and Water Resources Management / / Fi-John Chang, Li-Chiu Chang, Jui-Fa Chen, editors
Artificial Intelligence Techniques in Hydrology and Water Resources Management / / Fi-John Chang, Li-Chiu Chang, Jui-Fa Chen, editors
Pubbl/distr/stampa Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023
Descrizione fisica 1 online resource (302 pages)
Disciplina 551.48
Soggetto topico Hydrology
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910729782003321
Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Flood Forecasting Using Machine Learning Methods / Li-Chiu Chang, Fi-John Chang, Kuolin Hsu
Flood Forecasting Using Machine Learning Methods / Li-Chiu Chang, Fi-John Chang, Kuolin Hsu
Autore Chang Li-Chiu
Pubbl/distr/stampa Basel, Switzerland : , : MDPI, , 2019
Descrizione fisica 1 online resource (1 p.)
Soggetto non controllato data science; big data; artificial intelligence; soft computing; extreme event management; time series prediction; LSTM; rainfall-runoff; flood events; flood forecasting; data assimilation; particle filter algorithm; micro-model; Lower Yellow River; ANN; hydrometeorology; flood forecasting; real-time; postprocessing; machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps - data scarce basins; runoff series; data forward prediction; ensemble empirical mode decomposition (EEMD); stopping criteria; method of tracking energy differences (MTED); deep learning; convolutional neural networks; superpixel; urban water bodies; high-resolution remote-sensing images; monthly streamflow forecasting; artificial neural network; ensemble technique; phase space reconstruction; empirical wavelet transform; hybrid neural network; flood forecasting; self-organizing map; bat algorithm; particle swarm optimization; flood routing; Muskingum model; machine learning methods; St. Venant equations; rating curve method; nonlinear Muskingum model; hydrograph predictions; flood routing; Muskingum model; hydrologic models; improved bat algorithm; Wilson flood; Karahan flood; flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed; ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX); ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity; flood forecasting; extreme learning machine (ELM); backtracking search optimization algorithm (BSA); the upper Yangtze River; deep learning; LSTM network; water level forecast; the Three Gorges Dam; Dongting Lake; Muskingum model; wolf pack algorithm; parameters; optimization; flood routing; flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning; flood prediction; flood forecasting; hydrologic model; rainfall-runoff - hybrid & ensemble machine learning; artificial neural network; support vector machine; natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision tree; survey; classification and regression trees (CART)
ISBN 9783038975496
3038975494
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910765788503321
Chang Li-Chiu  
Basel, Switzerland : , : MDPI, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui