Reprint
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
Edited by
August 2021
254 pages
- ISBN978-3-0365-1720-9 (Hardback)
- ISBN978-3-0365-1719-3 (PDF)
This book is a reprint of the Special Issue Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management that was published in
Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary
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.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; artificial neural network; uncertainty; sustainability; prediction intervals; ungauged basin; machine learning; 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