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Practical Data‐Driven Flood Forecasting Based on Dynamical Systems Theory
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A review of advances in flash flood forecasting
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The 2017 July Northern Kyushu Torrential Rainfall Disaster—Geotechnical and Geological Perspectives
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Detecting strange attractors in turbulence
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Delay Embeddings for Forced Systems. I. Deterministic Forcing
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Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction
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Analysis and prediction of chaos in rainfall and stream flow time series
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SVR-based prediction of evaporation combined with chaotic approach
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Phase-space analysis of daily streamflow: characterization and prediction
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A comparison of nonlinear flood forecasting methods
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Nonlinear analysis of river flow time sequences
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Forecasting high-dimensional dynamics exploiting suboptimal embeddings
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Rainfall-Runoff Modeling Using Artificial Neural Networks
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Approximating high-dimensional dynamics by barycentric coordinates with linear programming
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A pilot study of river flow prediction in urban area based on phase space reconstruction
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Combining multiple forecasts for multivariate time series via state-dependent weighting
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Randomly distributed embedding making short-term high-dimensional data predictable
inferredcites →https://doi.org/10.1080/02626667.2011.637043openalex-snapshot
Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach
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Predicting chaotic time series
inferredcites →https://doi.org/10.1109/bigmm.2017.29openalex-snapshot
A Flood Forecasting Model Based on Deep Learning Algorithm via Integrating Stacked Autoencoders with BP Neural Network
inferredcites →https://doi.org/10.1109/ijcnn.2005.1556324openalex-snapshot
A comparative study of artificial neural network techniques for river stage forecasting
inferredcites →https://doi.org/10.1126/science.aag0863openalex-snapshot
Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality
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A training algorithm for optimal margin classifiers
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Atmospheric Predictability as Revealed by Naturally Occurring Analogues
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Generalized Theorems for Nonlinear State Space Reconstruction
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Using support vector machines for long-term discharge prediction
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DEVELOPMENT OF THE REAL-TIME RIVER STAGE PREDICTION METHOD USING DEEP LEARNING
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HYBRID DEEP NEURAL NETWORK AND DISTRIBUTED RAINFALL-RUNOFF MODEL FOR THE REAL-TIME RIVER STAGE PREDICTION
inferredcites →https://doi.org/10.2208/jscejhe.74.5_i_163openalex-snapshot
HIGH-RESOLUTION FLOW DIRECTION MAP OF JAPAN
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Flood Prediction Using Machine Learning Models: Literature Review
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Nonlinear Dynamics and Chaos with Applications to Hydrodynamics and Hydrological Modelling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Informed Neural Networks for Flood Forecasting With Limited Amount of Training Data
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FLOOD RISK PROBABILITY PREDICTION USING A RAINFALL INDEX EQUIVALENT WARNING LEVEL 4 FOR EACH RIVER SYSTEM
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TIDE LEVEL PREDICTION FOCUSING ON STORM SURGE AND SEICHE BY DYNAMICAL SYSTEMS THEORY
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Hybrid-input ensemble neural network for improving flood-forecasting performance
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Deep Convolutional LSTM for improved flash flood prediction
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