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Marco Franchini   Professor  University Educator/Researcher 
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Marco Franchini published an article in September 2018.
Top co-authors See all
Dragan A. Savic

277 shared publications

Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, UK

Vijay P. Singh

272 shared publications

Distinguished Professor, Regents Professor and Caroline and William N. Lehrer Distinguished Chair, Water Engineering, Dept. of Biological and Agriculture Engineering and Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843-2117

Luca Brocca

154 shared publications

Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy

Tommaso Moramarco

117 shared publications

IRPI, Consiglio Nazionale delle Ricerche, via Madonna Alta 126, 06128 Perugia, Italy

Zoran Kapelan

104 shared publications

Professor, Centre for Water Systems, College of Engineering, Mathematics, and Physical Sciences, Univ. of Exeter, North Park Rd., Harrison Bldg., Exeter EX4 4QF, UK

Publication Record
Distribution of Articles published per year 
(1991 - 2018)
Total number of journals
published in
Publications See all
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Hydrological Modelling of the Cascina Scala Catchment Enrico Creaco, Sara Todeschini, Marco Franchini Published: 20 September 2018
doi: 10.29007/d2g9
DOI See at publisher website
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Preliminary GIS Elaborations to Apply Rapid Flood Spreading Models Giulia Farina, Anna Bernini, Stefano Alvisi, Marco Franchini Published: 20 September 2018
doi: 10.29007/wdn6
DOI See at publisher website
Article 0 Reads 0 Citations From Water Consumption Smart Metering to Leakage Characterization at District and User Level: The GST4Water Project Chiara Luciani, Francesco Casellato, Stefano Alvisi, Marco F... Published: 30 July 2018
Proceedings, doi: 10.3390/proceedings2110675
DOI See at publisher website ABS Show/hide abstract
This paper presents some of the results achieved within the framework of the GST4Water project concerning the development of a real time monitoring and processing system for water consumption at individual user level. The system is based on the most innovative technologies proposed by the ICT sector and allows for receiving consumption data sent by a generic smart-meter installed in an user’s house and transfer them to a cloud platform. Here, the consumption data are stored and processed in order to characterize leakage at district meter area (DMA) and at individual user level. Finally, the processed data, on the one hand, are returned to the Water Utility and can be used for billing, on the other hand, they provide frequent feedback to the user thus gaining full awareness of his consumption behaviour.
Article 3 Reads 4 Citations Unsteady Flow Modeling of Pressure Real-Time Control in Water Distribution Networks Enrico Creaco, Alberto Campisano, Marco Franchini, Carlo Mod... Published: 01 September 2017
Journal of Water Resources Planning and Management, doi: 10.1061/(ASCE)WR.1943-5452.0000821
DOI See at publisher website
Article 0 Reads 0 Citations A robust approach based on time variable trigger levels for pump control Stefano Alvisi, Marco Franchini Published: 05 August 2017
Journal of Hydroinformatics, doi: 10.2166/hydro.2017.141
DOI See at publisher website
Article 5 Reads 4 Citations A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain Francesca Gagliardi, Stefano Alvisi, Zoran Kapelan, Marco Fr... Published: 12 July 2017
Water, doi: 10.3390/w9070507
DOI See at publisher website ABS Show/hide abstract
This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods), were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.