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CRML: A Convolution Regression Model With Machine Learning for Hydrology Forecasting

https://doi.org/10.1109/access.2019.2941234
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Weighted-average least squares estimation of generalized linear models
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Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques
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Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS
inferredcites →https://doi.org/10.1016/j.jhydrol.2014.03.008openalex-snapshot
Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
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Quadratic interpolation based teaching-learning-based optimization for chemical dynamic system optimization
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Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction
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A general theory of the unit hydrograph
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Flash Flood Forecasting: An Ingredients-Based Methodology
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Data-driven approaches to built environment flood resilience: A scientometric and critical review
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State of the Art Techniques for Flood Prediction Models - a Systematic Review
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Development of web‐based hydrograph analysis tool considering seasonality and flow condition
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A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting
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Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area
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