Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.

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Bibliographische Detailangaben
Format: E-Book
Sprache:Englisch
Veröffentlicht: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Schlagworte:
additive regression
artificial intelligence
artificial neural network
atmospheric reanalysis
bagging
Bayesian model averaging
big data
calibration
CWP
dagging
Daymet V3
EEFlux
ensemble modeling
extension principle
flood routing
fuzzy sets and systems
Google Earth Engine
Govindpur
groundwater
groundwater level prediction
hydroinformatics
hydrologic model
improved extreme learning machine (IELM)
irrigation performance
Kernel extreme learning machines
M5 model tree
machine learning
multivariate adaptive regression spline
Muskingum method
n/a
NDVI
neural network
nitrogen compound
nitrogen prediction
non-linear modeling
PACF
particle swarm optimization
prediction intervals
prediction models
principal component analysis
random subspace
Reference, Information and Interdisciplinary subjects
Research and information: general
rotation forest
satellite precipitation
sensitivity analysis
shortwave radiation flux density
South Korea
spatiotemporal variation
streamflow forecasting
streamflow simulation
support vector machine
sustainability
sustainable development
SVM-LF
SVM-RF
uncertainty
uncertainty analysis
ungauged basin
WANN
water conservation
water resources
ISBN:3036517200, 3036517197, 9783036517209, 9783036517193
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.
ISBN:3036517200
3036517197
9783036517209
9783036517193
DOI:10.3390/books978-3-0365-1719-3