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Effective forecasting of hourly typhoon rainfall using support vector machines

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What this work says

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

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Application of an artificial neural network to typhoon rainfall forecasting
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Shared near neighbours neural network model: a debris flow warning system
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A systematic approach to the input determination for neural network rainfall–runoff models
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The Nature of Statistical Learning Theory
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Potential assessment of the support vector regression technique in rainfall forecasting
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A non-linear rainfall-runoff model using radial basis function network
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An improved neural network approach to the determination of aquifer parameters
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Support vector regression for real-time flood stage forecasting
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Counterpropagation fuzzy-neural network for city flood control system
inferredcites →https://doi.org/10.1016/j.jhydrol.2009.03.032openalex-snapshot
Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods
inferredcites →https://doi.org/10.1016/s0895-7177(00)00272-7openalex-snapshot
An application of artificial neural networks for rainfall forecasting
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Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series
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Sparse Bayesian learning machine for real‐time management of reservoir releases
inferredcites →https://doi.org/10.1029/2006wr005383openalex-snapshot
Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling
inferredcites →https://doi.org/10.1029/2008wr006855openalex-snapshot
Using oceanic‐atmospheric oscillations for long lead time streamflow forecasting
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Stochastic Fuzzy Neural Network: Case Study of Optimal Reservoir Operation
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Optimization of Hedging Rules for Reservoir Operations
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Artificial Neural Networks in Hydrology. I: Preliminary Concepts
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Statistical Learning Theory
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Determination of aquifer parameters using radial basis function network approach
inferredcites →https://doi.org/10.1111/j.1752-1688.2002.tb01544.xopenalex-snapshot
FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1
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Tidal-Level Forecasting and Filtering by Neural Network Model
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EC-SVM approach for real-time hydrologic forecasting
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Flow categorization model for improving forecasting

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