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Flood Forecasting Using Machine Learning Methods
Flood Forecasting Using Machine Learning Methods
Autore Chang Fi-John
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (376 p.)
Soggetto non controllato natural hazards &
artificial neural network
flood routing
the Three Gorges Dam
backtracking search optimization algorithm (BSA)
lag analysis
artificial intelligence
classification and regression trees (CART)
decision tree
real-time
optimization
ensemble empirical mode decomposition (EEMD)
improved bat algorithm
convolutional neural networks
ANFIS
method of tracking energy differences (MTED)
adaptive neuro-fuzzy inference system (ANFIS)
recurrent nonlinear autoregressive with exogenous inputs (RNARX)
disasters
flood prediction
ANN-based models
flood inundation map
ensemble machine learning
flood forecast
sensitivity
hydrologic models
phase space reconstruction
water level forecast
data forward prediction
early flood warning systems
bees algorithm
random forest
uncertainty
soft computing
data science
hydrometeorology
LSTM
rating curve method
forecasting
superpixel
particle swarm optimization
high-resolution remote-sensing images
machine learning
support vector machine
Lower Yellow River
extreme event management
runoff series
empirical wavelet transform
Muskingum model
hydrograph predictions
bat algorithm
data scarce basins
Wilson flood
self-organizing map
big data
extreme learning machine (ELM)
hydroinformatics
nonlinear Muskingum model
invasive weed optimization
rainfall–runoff
flood forecasting
artificial neural networks
flash-flood
streamflow predictions
precipitation-runoff
the upper Yangtze River
survey
parameters
Haraz watershed
ANN
time series prediction
postprocessing
flood susceptibility modeling
rainfall-runoff
deep learning
database
LSTM network
ensemble technique
hybrid neural network
self-organizing map (SOM)
data assimilation
particle filter algorithm
monthly streamflow forecasting
Dongting Lake
machine learning methods
micro-model
stopping criteria
Google Maps
cultural algorithm
wolf pack algorithm
flood events
urban water bodies
Karahan flood
St. Venant equations
hybrid &
hydrologic model
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910346688303321
Chang Fi-John  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
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