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Palabras clave – provistas por la editorial

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