top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.
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
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
Autore Kisi Ozgur
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (238 p.)
Soggetto topico Research & information: general
Soggetto non controllato groundwater
artificial intelligence
hydrologic model
groundwater level prediction
machine learning
principal component analysis
spatiotemporal variation
uncertainty analysis
hydroinformatics
support vector machine
big data
artificial neural network
nitrogen compound
nitrogen prediction
prediction models
neural network
non-linear modeling
PACF
WANN
SVM-LF
SVM-RF
Govindpur
streamflow forecasting
Bayesian model averaging
multivariate adaptive regression spline
M5 model tree
Kernel extreme learning machines
South Korea
uncertainty
sustainability
prediction intervals
ungauged basin
streamflow simulation
satellite precipitation
atmospheric reanalysis
ensemble modeling
additive regression
bagging
dagging
random subspace
rotation forest
flood routing
Muskingum method
extension principle
calibration
fuzzy sets and systems
particle swarm optimization
EEFlux
irrigation performance
CWP
water conservation
NDVI
water resources
Daymet V3
Google Earth Engine
improved extreme learning machine (IELM)
sensitivity analysis
shortwave radiation flux density
sustainable development
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557448103321
Kisi Ozgur  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui