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Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
Ozgur Kisi (editor)
2021
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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.
This book is included in DOAB.
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Keywords
- 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 & interdisciplinary subjects
- Research & 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
Links
DOI: 10.3390/books978-3-0365-1719-3Editions