Interval Forecast of Water Quality Parameters

Authors:
Orlando Ohashi;Luís Torgo;Rita P. Ribeiro
Affiliations:
LIAAD INESC Porto L.A. / Faculty of Sciences-University of Porto. Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal. Emails: {ohashi, ltorgo, rpribeiro}@liaad.up.pt;LIAAD INESC Porto L.A. / Faculty of Sciences-University of Porto. Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal. Emails: {ohashi, ltorgo, rpribeiro}@liaad.up.pt;LIAAD INESC Porto L.A. / Faculty of Sciences-University of Porto. Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal. Emails: {ohashi, ltorgo, rpribeiro}@liaad.up.pt
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

Citing 7
Cited 0

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Random Forests

Machine Learning
An environmental sensor network to determine drinking water quality and security

ACM SIGMOD Record
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Quantile Regression Forests

The Journal of Machine Learning Research
High-quantile modeling for customer wallet estimation and other applications

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A simple quantile regression via support vector machine

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The current quality control methodology adopted by the water distribution service provider in the metropolitan region of Porto-Portugal, is based on simple heuristics and empirical knowledge. Based on the domain complexity and data volume, this application is a perfect candidate to apply data mining process. In this paper, we propose a new methodology to predict the range of normality for the values of different water quality parameters. These intervals of normality are of key importance to decide on costly inspection activities. Our experimental evaluation confirms that our proposal achieves good results on the task of forecasting the normal distribution of values for the following 30 days. The proposed method can be applied to other domains with similar network monitoring objectives.