13th International Conference on Hydroinformatics - HIC 2018 PDF Print E-mail

LogoHIC2018

 

Palermo 1-6 July 2018

The University of Palermo and the University of Enna “Kore” are pleased to invite the hydroinformatics international community to the 13th International Conference on Hydroinformatics (HIC 2018).

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Hydroinformatics tool assistance PDF Print E-mail

Some security updates sent by Microsoft on 09 December 2014, inhibited ActiveX push-button in

EPR-MOGA-XL, ANN-MOGA-XL and WDNetXL command steets.

ingranaggi See problem details and solution.

 
Mini-Symposium on Evolutionary Polynomial Regression at HIC 2014 PDF Print E-mail

We are pleased to announce that the upcoming International Conference on Hydroinformatics (HIC 2014), to be held in New York (USA), 17-21 August 2014will host a Mini-Symposium on “Evolutionary Polynomial Regression for data-mining application in Hydro-Engineering”.

flagnew see the Official Announcement (pdf) 

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ANN MOGA - XL is on-line! PDF Print E-mail

ANN MOGA-XL

MOGA Artificial Neural Networks tool is on Excel now!

 

The Artificial Neural Networks by Multi Objective Genetic algorithms (ANN MOGA) [Giustolisi and Simeone, 2006] is a tool based on the homonymous modelling methodology based on the ANNs paradigm . The tool employs a particular structure of ANN named the Input-Output Neural Network (IONN).

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SINTEF chooses WDNetXL for Oslo water distribution network PDF Print E-mail

Since the presentation in Rome (June 2011) the WDNetXL system has received an unpredictable approval by the international community from both academic and professional side.

The latest demonstration comes from Norway, where SINTEF decided to use the WDNetXL system in a project pertaining the analysis and management of the water distribution network in Oslo.

(see the paper on ingegneri.info)

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EPR MOGA - XL is on-line! PDF Print E-mail

EPR MOGA-XL

Multi Objective Evolutionary Polynomial Regression tool is on Excel now!

 

The Evolutionary Polynomial Regression (EPR) [Giustolisi and Savic, 2006] has been introduced in the hydroinformatics community as a hybrid data-driven technique, which combines the effectiveness of genetic algorithms with numerical regression for developing simple and easily interpretable mathematical model expressions.

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