C. Wu
Environmental Engineering · with Chau · 2005–2013
A researcher in the Science corpus: 7 papers; trusts 27 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 52 citations into it; #1,118 of 5,479 by trust here, #1,116 by citations
- Water Science and Technology — 36 citations into it; #511 of 1,711 by trust here, #564 by citations
- Global and Planetary Change — 27 citations into it; #4,164 of 10,225 by trust here, #3,676 by citations
- Artificial Intelligence — 19 citations into it
- Electrical and Electronic Engineering — 13 citations into it; #7,739 of 16,539 by trust here, #12,727 by citations
- Analytical Chemistry — 5 citations into it
Whom C. Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dimitri P. Solomatine
- Wen Wang
- Ozgur Kisi
- Vojislav Kecman
- K. Sudheer
- Gavin J. Bowden
- P. Gelder
- Yunpeng Xue
- Stéphane Mallat
- Mehmet Emre Cek
Who trusts C. Wu overall
- Ravinesh C. Deo
- Jan Adamowski
- Ozgur Kisi
- Yaoyao He
- Isa Ebtehaj
- Vahid Nourani
- Kwok-Wing Chau
- Qi Feng
- Jürgen Geiser
- Mukesh Tiwari
Papers from C. Wu's perspective
Ordered by how strongly the researchers C. Wu trusts endorse each paper. Same corpus, a different order for every researcher.
- M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China
- Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (Environmental Modelling & Software)
- Input determination for neural network models in water resources applications. Part 1—background and methodology
- The Nature of Statistical Learning Theory
- Machine learning approaches for estimation of prediction interval for the model output (Neural Networks)
- Multilayer feedforward networks are universal approximators
- Analysis of observed chaotic data
- Genetic Algorithms in Search Optimization and Machine Learning
- Model Induction with Support Vector Machines: Introduction and Applications
- A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models
Their papers
- Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010) — 362 citations
- Prediction of rainfall time series using modular soft computingmethods (2013) — 298 citations
- Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis (2011) — 364 citations
- Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009) — 414 citations
- Methods to improve neural network performance in daily flows prediction (2009) — 310 citations
- Mathematical model of water quality rehabilitation with rainwater utilisation: a case study at Haigang (2006) — 106 citations
- Comparison of Several Flood Forecasting Models in Yangtze River (2005) — 339 citations