Hong Zhang

Orcid: 0000-0002-2642-5467

Affiliations:
  • Griffith University, Griffith School of Engineering, Gold Coast Campus, Queensland, Australia


According to our database1, Hong Zhang authored at least 12 papers between 2006 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
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PhD thesis 
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Links

Online presence:

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Bibliography

2021
A three-dimensional manganese model for the management of a monomictic drinking water reservoir.
Environ. Model. Softw., 2021

Coupled data-driven and process-based model for fluorescent dissolved organic matter prediction in a shallow subtropical reservoir.
Environ. Model. Softw., 2021

2018
Integrated intelligent water-energy metering systems and informatics: Visioning a digital multi-utility service provider.
Environ. Model. Softw., 2018

Re-engineering traditional urban water management practices with smart metering and informatics.
Environ. Model. Softw., 2018

2017
Smart Technologies in Reducing Carbon Emission: Artificial Intelligence and Smart Water Meter.
Proceedings of the 9th International Conference on Machine Learning and Computing, 2017

2016
Hybrid water treatment cost prediction model for raw water intake optimization.
Environ. Model. Softw., 2016

2015
An autonomous decision support system for manganese forecasting in subtropical water reservoirs.
Environ. Model. Softw., 2015

Intelligent autonomous system for residential water end use classification: Autoflow.
Appl. Soft Comput., 2015

2014
An autonomous and intelligent expert system for residential water end-use classification.
Expert Syst. Appl., 2014

2013
An intelligent pattern recognition model to automate the categorisation of residential water end-use events.
Environ. Model. Softw., 2013

2008
A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth.
Proceedings of the Engineering Evolutionary Intelligent Systems, 2008

2006
Improvement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction.
Proceedings of the International Joint Conference on Neural Networks, 2006


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