A novel computer-aided multivariate water quality index

  • Siong Fong Sim
  • Teck Yee Ling
  • Seng Lau
  • Mohd Zuli Jaafar
Article
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Abstract

A computer-aided multivariate water quality index is developed based on partial least squares (PLS) regression. The index is termed as the partial least squares water quality index (PLS-WQI). Briefly, a training set was computationally generated based on the guideline of National Water Quality Standards for Malaysia (NWQS) to predict the water quality. The index is benchmarked with the well-established index developed by the Department of Environment, Malaysia (DOE-WQI). The PLS-WQI is a continuous variable with the value closer to I indicating good water quality and closer to V indicating poor water quality. Unlike other conventional indexing methods, the algorithm calculates the index in a multivariate manner. The algorithm allows rapid processing of a large dataset without tedious calculation; it can be an efficient tool for spatial and temporal routine monitoring of water quality. Although the algorithm is designed based on the guideline of NWQS, it can be easily adapted to accommodate other guidelines. The algorithm was evaluated and demonstrated on the simulated and real datasets. Results indicate that the algorithm is robust and reliable. Based on six parameters, the overall ratings derived are inversely correlated to DOE-WQI. When the number of parameter is increased, the overall ratings appear to provide better insights into the water quality.

Keywords

Multivariate Partial least squares regression Water quality index National Water Quality Standards for Malaysia 
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Notes

Acknowledgments

The authors thank the Ministry of Education, Malaysia, for funding this project (ERGS/STWN(01)1015/2013(12)). The authors also thank the Natural Resources and Environment Board (NREB) Sarawak for providing Dataset 2 for evaluation of the algorithm.

Compliance with ethical standards

The authors have declared that no competing interest exists.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Siong Fong Sim
    • 1
  • Teck Yee Ling
    • 1
  • Seng Lau
    • 1
  • Mohd Zuli Jaafar
    • 2
  1. 1.Faculty of Resource Science and TechnologyUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.Faculty of Applied ScienceUniversiti Teknologi MARAShah AlamMalaysia

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