Title: Water quality detection based on a data mining process on the California estuary

Authors: Edwin Castillo; David Camilo Corrales; Emmanuel Lasso; Agapito Ledezma; Juan Carlos Corrales

Addresses: Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán Cauca, Colombia ' Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán Cauca, Colombia; Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 – Leganés, Spain ' Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán Cauca, Colombia ' Departamento de Ciencias de la Computación e Ingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 – Leganés, Spain ' Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán Cauca, Colombia

Abstract: Freshwater is considered one of the most important renewable natural resources of the planet. In this sense, it is vital to study and evaluate the water quality in rivers and basins. The USA and especially the border states like California face the same water problems as its southern neighbours, such as the deterioration of public drinking water systems and the continued appearance of pollutants that threaten domestic water sources. This implies the need to monitor and analyse the water supplies in each region. Several researches have been conducted to develop water quality detection systems through supervised learning algorithms. However, these research approaches set aside the data processing to improve the performance of supervised learning algorithms. This paper presents an improvement of data processing techniques for a water quality detection system based on supervised learning and data quality techniques for the California estuary.

Keywords: water quality; data mining; data processing; lotic ecosystem; dimensionality reduction; supervised learning; principal component analysis; PCA; boosting; synthetic minority over-sampling technique; SMOTE.

DOI: 10.1504/IJBIDM.2017.086987

International Journal of Business Intelligence and Data Mining, 2017 Vol.12 No.4, pp.406 - 424

Received: 26 Jan 2017
Accepted: 03 Feb 2017
Published online: 03 Oct 2017 *

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