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Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin
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Author(s): Jose Simmonds (Universidad Carlos III de Madrid, Leganés, Spain), Juan A. Gómez (Universidad de Panamá, Panama City, Panama)and Agapito Ledezma (Universidad Carlos III de Madrid, Leganés, Spain)
Copyright: 2018
Volume: 9
Issue: 2
Pages: 19
Source title:
International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Frederic Andres (National Institute of Informatics, Japan), Chutiporn Anutariya (Asian Institute of Technology, Thailand), Teeradaj Racharak (Japan Advanced Institute of Science and Technology, Japan)and Watanee Jearanaiwongkul (National institute of Informatics, Japan)
DOI: 10.4018/IJAEIS.2018040101
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Abstract
This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.
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