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Data Driven Hydrological Model for River Level Dynamics Forecasting in Uruguay Rio Negro Basin
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Pattern Recognition and Machine Learning
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Evidence for deterministic chaos in long-term high-resolution karstic streamflow time series
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Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction
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A practical method for calculating largest Lyapunov exponents from small data sets
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A Review on Hydrological Models
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Introductory overview: Error metrics for hydrologic modelling – A review of common practices and an open source library to facilitate use and adoption
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The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology
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Remobilization of pollutants during extreme flood events poses severe risks to human and environmental health
inferredcites →https://doi.org/10.1016/j.jhydrol.2018.06.072openalex-extracted
Temporal streamflow analysis: Coupling nonlinear dynamics with complex networks
inferredcites →https://doi.org/10.1016/s0022-1694(02)00112-9openalex-extracted
River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches
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A semi-distributed approach to rainfall-runoff modelling—a case study in a snow affected catchment
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Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach
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Observing Rivers With Varying Spatial Scales
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Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model
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Satellite imaging reveals increased proportion of population exposed to floods
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IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
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Interspike interval embedding of chaotic signals
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Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks
inferredcites →https://doi.org/10.1175/jhm-d-12-010.1openalex-extracted
Combining CMORPH and Rain Gauges Observations over the Rio Negro Basin
inferredcites →https://doi.org/10.1177/030913330102500104openalex-extracted
Hydrological modelling using artificial neural networks
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Strategies for smarter catchment hydrology models: incorporating scaling and better process representation
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Embedding of sequences of time intervals
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The MGB-IPH model for large-scale rainfall—runoff modelling
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COMBINING RAINFALL AND WATER LEVEL DATA FOR MULTISTEP HIGH TEMPORAL RESOLUTION EMPIRICAL HYDROLOGICAL FORECASTING
inferredcites →https://doi.org/10.31223/osf.io/euqmxopenalex-extracted
Observing Rivers with Varying Spatial Scales
inferredcites →https://doi.org/10.3390/sym14101986openalex-extracted
Stability Analysis of an Upwind Difference Splitting Scheme for Two-Dimensional Saint–Venant Equations
inferredcites →https://doi.org/10.3390/w12092571openalex-extracted
Evaluation of Satellite Precipitation Products for Hydrological Modeling in the Brazilian Cerrado Biome
inferredcites →https://doi.org/10.3390/w13040533openalex-extracted
Operational Implementation of Satellite-Rain Gauge Data Merging for Hydrological Modeling
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A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks
inferredcites →https://doi.org/10.3390/w15132323openalex-extracted
Reservoir Reliability as Affected by Climate Change and Strategies for Adaptation
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Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.5194/hess-23-1281-2019openalex-extracted
Conservative finite-volume forms of the Saint-Venant equations for hydrology and urban drainage
inferredcites →https://doi.org/10.5194/hess-23-5089-2019openalex-extracted
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
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