On the public citation graph
LIQUID TIME-CONSTANT NETWORKS FOR URBAN DRAINAGE FLOOD PREDICTION: ADAPTIVE CONTINUOUS-TIME DYNAMICS FOR MULTI-SCALE HYDROLOGICAL PROCESSES
0 signed · 31 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s13753-024-00594-2openalex-snapshot
Enhancing Road Drainage Systems for Extreme Storms: Integration of a High-Precision Flow Diversion Module into SWMM Code
inferredcites →https://doi.org/10.1016/0022-1694(70)90255-6openalex-snapshot
River flow forecasting through conceptual models part I — A discussion of principles
inferredcites →https://doi.org/10.1016/j.jhydrol.2006.02.013openalex-snapshot
Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales
inferredcites →https://doi.org/10.1016/j.jhydrol.2007.02.015openalex-snapshot
A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged locations
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1016/j.watres.2022.118972openalex-snapshot
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning
inferredcites →https://doi.org/10.1016/j.watres.2024.122142openalex-snapshot
Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
inferredcites →https://doi.org/10.1029/1998wr900018openalex-snapshot
Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
inferredcites →https://doi.org/10.1061/jyceaj.0000104openalex-snapshot
Synthetic Storm Pattern for Drainage Design
inferredcites →https://doi.org/10.1080/02626667.2022.2145200openalex-snapshot
Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps
inferredcites →https://doi.org/10.1126/scirobotics.adc8892openalex-snapshot
Robust flight navigation out of distribution with liquid neural networks
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1198/073500102753410444openalex-snapshot
Comparing Predictive Accuracy
inferredcites →https://doi.org/10.1609/aaai.v35i9.16936openalex-snapshot
Liquid Time-constant Networks
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.2166/hydro.2023.001openalex-snapshot
Deep learning model on rates of change for multi-step ahead streamflow forecasting
inferredcites →https://doi.org/10.2166/nh.2024.035openalex-snapshot
A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation
inferredcites →https://doi.org/10.2307/3001968openalex-snapshot
Individual Comparisons by Ranking Methods
inferredcites →https://doi.org/10.30564/jees.v7i6.9437openalex-snapshot
Study on Response Capacity of Drainage Systems in Coastal Urban of Vietnam Under the Impact of Extreme Weather Events
inferredcites →https://doi.org/10.3354/cr030079openalex-snapshot
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
inferredcites →https://doi.org/10.3390/app15137400openalex-snapshot
A Methodology to Assess the Effectiveness of SUDSs Under Climate Change Scenarios at Urban Scale: Application to Bari (Italy)
inferredcites →https://doi.org/10.3390/modelling3040030openalex-snapshot
Efficient Hydrodynamic Modelling of Urban Stormwater Systems for Real-Time Applications
inferredcites →https://doi.org/10.3390/w17030405openalex-snapshot
Comparison of the Effectiveness of Artificial Neural Networks and Elastic Net Regression in Surface Runoff Modeling
inferredcites →https://doi.org/10.3390/w17121820openalex-snapshot
Rapid SWMM Catchment Prototyping Using Fuzzy Logic: Analyzing Catchment Features for Enhanced Efficiency
inferredcites →https://doi.org/10.5194/hess-23-5089-2019openalex-snapshot
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
inferredcites →https://doi.org/10.5194/hess-26-4013-2022openalex-snapshot
Flood forecasting with machine learning models in an operational framework
inferredcites →https://doi.org/10.5194/hess-26-5879-2022openalex-snapshot
All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling
inferredcites →https://doi.org/10.5194/hess-27-2035-2023openalex-snapshot
An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.3390/rs18183116openalex-snapshot
Adaptive Neural Network Approaches in Remote Sensing Imagery: A Systematic Review
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.