Robust predictive modelling of water pollution using biomarker data

This source preferred by Marcin Budka

Authors: Budka, M., Gabrys, B. and Ravagnan, E.

http://eprints.bournemouth.ac.uk/11967/

Journal: Water Research

Volume: 44

Pages: 3294-3308

ISSN: 0043-1354

DOI: 10.1016/j.watres.2010.03.006

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data.

A step–by–step, systematic data analysis approach is presented, resulting in design of a purely data–driven model, able to accurately discriminate between various coastal water pollution levels.

The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required ‘under the hood’ knowledge.

Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties.

The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.

This data was imported from PubMed:

Authors: Budka, M., Gabrys, B. and Ravagnan, E.

http://eprints.bournemouth.ac.uk/11967/

Journal: Water Res

Volume: 44

Issue: 10

Pages: 3294-3308

eISSN: 1879-2448

DOI: 10.1016/j.watres.2010.03.006

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties. The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.

This data was imported from Scopus:

Authors: Budka, M., Gabrys, B. and Ravagnan, E.

http://eprints.bournemouth.ac.uk/11967/

Journal: Water Research

Volume: 44

Issue: 10

Pages: 3294-3308

ISSN: 0043-1354

DOI: 10.1016/j.watres.2010.03.006

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels.The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties.The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities. © 2010 Elsevier Ltd.

This data was imported from Web of Science (Lite):

Authors: Budka, M., Gabrys, B. and Ravagnan, E.

http://eprints.bournemouth.ac.uk/11967/

Journal: WATER RESEARCH

Volume: 44

Issue: 10

Pages: 3294-3308

ISSN: 0043-1354

DOI: 10.1016/j.watres.2010.03.006

This data was imported from Europe PubMed Central:

Authors: Budka, M., Gabrys, B. and Ravagnan, E.

http://eprints.bournemouth.ac.uk/11967/

Journal: Water research

Volume: 44

Issue: 10

Pages: 3294-3308

eISSN: 1879-2448

ISSN: 0043-1354

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties. The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.

The data on this page was last updated at 04:42 on September 20, 2017.