Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

• Format: eBook
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: 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

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.