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Marco Franchini   Professor  University Educator/Researcher 
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Marco Franchini published an article in September 2017.
Top co-authors See all
Vijay P Singh

265 shared publications

Distinguished Professor, Regents Professor and Caroline and William N. Lehrer Distinguished Chair in Water Engineering, Dept. of Biological and Agricultural Engineering and Zachry Dept. of Civil Engineering, Texas A&M Univ., 321 Scoates Hall, TAMU 2117, College Station, TX 77843-2117

Dragan Savic

205 shared publications

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

Luca Brocca

137 shared publications

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

Tommaso Moramarco

103 shared publications

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

82
Publications
104
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222
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Publication Record
Distribution of Articles published per year 
(1994 - 2017)
Total number of journals
published in
 
20
 
Publications See all
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 3 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.
Article 3 Reads 6 Citations A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data Elena Pacchin, Stefano Alvisi, Marco Franchini Published: 28 February 2017
Water, doi: 10.3390/w9030172
DOI See at publisher website ABS Show/hide abstract
In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand over the 24 h that follow the time the forecast is made and the average water demand over the 24 h that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-h forecasting period and the average water demand over that period. These coefficients are estimated using the information available in the weeks prior to the time of forecasting and, therefore, the model does not require any actual calibration process. The length of the time series necessary to implement the model is thus just a few weeks (3–4 weeks) and the model can be parameterized and used without there being any need to collect hourly water demand data for long periods. The application of the model to a real-life case and a comparison with results provided by another model already proposed in the scientific literature show it to be effective, robust, and easy to use.
CONFERENCE-ARTICLE 7 Reads 0 Citations <strong>A short-term water demand forecasting model based on a short moving window of previously observed data</strong> Elena Pacchin, Stefano Alvisi, Marco Franchini Published: 16 November 2016
The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-d002
DOI See at publisher website ABS Show/hide abstract

Short-term water demand forecasting is a useful tool for water distribution system management. In fact, an accurate prediction of water consumptions of a network or a part of it can support the scheduling of the main devices of the network, such as pumping stations or valves.

In this paper a model for short term water demand forecasting is proposed. The model is structured in order to provide at each hour the water demand forecast for the next 24 hour basing on coefficients estimated according to a short moving window of previously observed data.

More in details, the hourly forecast is performed in two steps: in the first step the average water demand for the next 24 hours (Q24) is forecasted multiplying the average water consumption observed in the last 24 hours by a previously estimated coefficient; in the second step, the water consumption of each of the next 24 hours is forecasted multiplying the forecasted Q24 by hourly coefficients. The coefficients’ values (both the one used to forecast the Q24 and those used to forecast the hourly values) are updated at each hour on the basis of the water demands observed in the last n (e.g. n=4) weeks.

The model is applied to a real case study; the analysis of the results, and their comparison with those provided by another short term water demand forecasting model already presented in the scientific literature, highlights that the proposed model provides an accurate and robust forecast, resulting in an efficient tool for real time management of water distribution networks requiring a very small effort for its parameterization.

Article 3 Reads 4 Citations Generalized Resilience and Failure Indices for Use with Pressure-Driven Modeling and Leakage Enrico Creaco, Marco Franchini, Ezio Todini Published: 01 August 2016
Journal of Water Resources Planning and Management, doi: 10.1061/(asce)wr.1943-5452.0000656
DOI See at publisher website
Article 3 Reads 1 Citation Multistep Approach for Optimizing Design and Operation of the C-Town Pipe Network Model Enrico Creaco, Stefano Alvisi, Marco Franchini Published: 01 May 2016
Journal of Water Resources Planning and Management, doi: 10.1061/(asce)wr.1943-5452.0000585
DOI See at publisher website
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