首页|Polytechnic University of Bari Reports Findings in Machine Learning (Using symbolic machine learning to assess and model substance transport and decay in water distribution networks)
Polytechnic University of Bari Reports Findings in Machine Learning (Using symbolic machine learning to assess and model substance transport and decay in water distribution networks)
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2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting out of Bari, Italy, by NewsRx editors, research stated, “Drinking water infrastructures are systems of pipes which are generally networked. They play a crucial role in transporting and delivering clean water to people.” Our news journalists obtained a quote from the research from the Polytechnic University of Bari, “The water quality analysis refers to the evaluation of the advective diffusion of any substance in drinking water infrastructures from source nodes. Such substances could be a contamination for the system or planned for the disinfection, e.g., chlorine. The water quality analysis is performed by integrating the differential equation in the pipes network domain using the kinetics of the substance decay and the Lagrangian scheme. The kinetics can be formulated using a specific reaction order depending on the substance characteristics. The basis for the integration is the pipes velocity field calculated by means of hydraulic analysis. The aim of the present work is to discover the intrinsic mechanism of the substance transport in drinking water infrastructures, i.e., their pipes network domain, using the symbolic machine learning, named Evolutionary Polynomial Regression, which provides ‘synthetic’ models (symbolic formulas) from data. We demonstrated, using one real network and two test networks, that the concentration at each node of the network can be predicted using the travel time along the shortest path(s) between the source and each node.”
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