A carregar...

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.

Na minha lista:

Detalhes bibliográficos
Autor Corporativo:	Kisi, Ozgur
Formato:	Livro
Idioma:	Inglês
Publicado em:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Assuntos:	thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general 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 n/a
Acesso em linha:	https://directory.doabooks.org/handle/20.500.12854/76675
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

Registos relacionados