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.
|Title of host publication||Proc. Workshop on Machine Learning Applications in real world|
|Publication status||Published - Dec 1998|
|Event||ICML 97 - Fourteenth International Conference on Machine Learning: Workshop on Machine Learning Applications in real world - Nashville, United States|
Duration: 8 Jul 1997 → 12 Jul 1997
|Conference||ICML 97 - Fourteenth International Conference on Machine Learning|
|Period||8/07/97 → 12/07/97|
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