Keywords

biofilm; drinking water distribution system; pre-processing; random forests; regression trees.

Location

Session C1: VI Data Mining for Environmental Sciences Session

Start Date

13-7-2016 2:30 PM

End Date

13-7-2016 2:50 PM

Description

Biofilm development in drinking water distribution systems (DWDSs) is a real problem negatively affecting service and water quality, and, thus, the satisfaction of the final consumers. It is the direct and indirect responsible for many of the DWDSs’ problems, and a lot of resources are invested to mitigate its effects. Addressing this problem has been a concern of researchers and DWDS managers for years. However, it is only recently that both technology and data have been available to support the new approach presented in this work. Our proposal is based on the combination of various existing data sets from similar studies to conduct a meta-data analysis of biofilm development. The approach lies on an intensive data pre-processing. Having a complete and extensive database on biofilm development in DWDSs allows applying Machine Learning techniques to develop a practical model. It is based on a multidisciplinary research vision to formulate effective biofilm control strategies. This work presents the basis for the development of a useful decision-making tool to assist in DWDS management. The negative effects on service and consumers caused by biofilm would be mitigated maintaining it at the lowest level. The performance of the suggested models is tested with data coming from two different case-studies: the DWDSs of the city of Thessaloniki (Greece) and the Pennine Water Group experimental facility (UK). The results obtained validate this methodology as an excellent approach to studying biofilm development in DWDSs.

 
Jul 13th, 2:30 PM Jul 13th, 2:50 PM

A Multi-disciplinary Procedure to Ascertain Biofilm Formation in Drinking Water Pipes

Session C1: VI Data Mining for Environmental Sciences Session

Biofilm development in drinking water distribution systems (DWDSs) is a real problem negatively affecting service and water quality, and, thus, the satisfaction of the final consumers. It is the direct and indirect responsible for many of the DWDSs’ problems, and a lot of resources are invested to mitigate its effects. Addressing this problem has been a concern of researchers and DWDS managers for years. However, it is only recently that both technology and data have been available to support the new approach presented in this work. Our proposal is based on the combination of various existing data sets from similar studies to conduct a meta-data analysis of biofilm development. The approach lies on an intensive data pre-processing. Having a complete and extensive database on biofilm development in DWDSs allows applying Machine Learning techniques to develop a practical model. It is based on a multidisciplinary research vision to formulate effective biofilm control strategies. This work presents the basis for the development of a useful decision-making tool to assist in DWDS management. The negative effects on service and consumers caused by biofilm would be mitigated maintaining it at the lowest level. The performance of the suggested models is tested with data coming from two different case-studies: the DWDSs of the city of Thessaloniki (Greece) and the Pennine Water Group experimental facility (UK). The results obtained validate this methodology as an excellent approach to studying biofilm development in DWDSs.