Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management (MDPI - Multidisciplinary Digital Publishing Institute, 2021)
Keywords: 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