Application of machine learning in water distribution networks: An initial study

Luís Manuel Camarinha-Matos, Fernando Martinelli

Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

Abstract

This paper describes an on going work on the application of machine learning techniques in the domain of water distribution networks. This research is performed in the framework of an European project called WATERNET, that aims to develop a system to control and manage water distribution networks and is composed by a supervision system, a distributed information management subsystem, an optimization subsystem, a water quality monitoring subsystem, a simulation subsystem, and a machine learning subsystem. This paper is focused in the machine learning subsystem, describing the approach followed and found difficulties. The basic raw material for this work are historical data from a Portuguese water distribution company that has 45 water stations and some of then with 6 years of collected data at every 5 minutes. The paper also shows the first results obtained, discusses difficulties found in the first experiments and introduces an architecture based on qualitative models/causal relationship to make more easy the process of knowledge extraction from the historical data and the assessment of the extracted knowledge.
Original languageEnglish
Title of host publicationProc. Workshop on Machine Learning Applications in real world
Pages49-57
DOIs
Publication statusPublished - Dec 1998
EventICML 97 - Fourteenth International Conference on Machine Learning: Workshop on Machine Learning Applications in real world - Nashville, United States
Duration: 8 Jul 199712 Jul 1997

Conference

ConferenceICML 97 - Fourteenth International Conference on Machine Learning
CountryUnited States
CityNashville
Period8/07/9712/07/97

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Camarinha-Matos, L. M., & Martinelli, F. (1998). Application of machine learning in water distribution networks: An initial study. In Proc. Workshop on Machine Learning Applications in real world (pp. 49-57) https://doi.org/10.1016/S1088-467X(98)00030-4