Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
Kisi, Ozgur
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (238 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1719-3 9783036517209 9783036517193
10.3390/books978-3-0365-1719-3 doi
Research & 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
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (238 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1719-3 9783036517209 9783036517193
10.3390/books978-3-0365-1719-3 doi
Research & 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