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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Format:	Buch
Sprache:	eng
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
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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.
DOI:	10.3390/books978-3-0365-1719-3