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Basin</style>Drupal-Biblio17<style face="normal" font="default" size="100%">A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Probabilistic urban water demand forecasting using wavelet-based machine learning models</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Data assimilation for streamflow forecasting using extreme learning machines</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms</style>Drupal-Biblio17<style face="normal" font="default" size="100%">A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Using a boundary-corrected wavelet transform coupled with machine learning and 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models</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Using Bootstrap ELM and LSSVM Models to Estimate River Ice Thickness in the Mackenzie River Basin in the Northwest Territories, Canada</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework</style>Drupal-Biblio32<style face="normal" font="default" size="100%">An Ensemble Wavelet-based Stochastic Data-driven Framework for Addressing Nonlinearity, Multiscale Change, and Uncertainty in Water Resources Forecasting</style>Drupal-Biblio47<style face="normal" font="default" size="100%">Information-theoretic-based input variable selection for hydrology and water resources</style>Drupal-Biblio47<style face="normal" font="default" size="100%">A stochastic data-driven forecasting framework using wavelets for forecasting uncertain hydrological and water resources processes</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model</style>Drupal-Biblio47<style face="normal" font="default" size="100%">Hydrological data assimilation using Extreme Learning Machines</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq</style>Drupal-Biblio17<style face="normal" font="default" size="100%">The application of dynamic linear bayesian models in hydrological forecasting: varying coefficient regression and discount weighted regression</style>Drupal-Biblio47<style face="normal" font="default" size="100%">Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods</style>Drupal-Biblio47<style face="normal" font="default" size="100%">Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method</style>Drupal-Biblio17<style face="normal" font="default" size="100%">Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS</style>Drupal-Biblio47<style 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