A multi-parameterized water quality prediction method with differential computing among sampling sites

Khoumkham Ladsavong, Petchporn Chawakitchareon, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a multi-parameterized water quality prediction method with differential computing among sampling sites at Bangkok City, Thailand. Here, two canals were selected for case study and nine parameters were chosen for water quality prediction, they are Temperature, pH, DO, BOD, COD, NH3-N, NO2-N, NO3-N, and TP. The data obtained from 2007 to November 2017. The differential computing is chosen to predict the parameters along sampling sites. The results are indicated the predictive values of temperature and pH are entirely accurate than another parameter because the error values are low values and both parameters are slightly changed from the past up to present. Therefore, the differential computing possibly uses to predict some water quality parameters which they are quite stable conditions.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXX
EditorsTatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
PublisherIOS Press
Pages195-207
Number of pages13
ISBN (Electronic)9781614999324
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume312
ISSN (Print)0922-6389

Keywords

  • Differential Computing
  • Surface Water Quality
  • Visualization of Multi-Parameter
  • Water Quality Prediction

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Ladsavong, K., Chawakitchareon, P., & Kiyoki, Y. (2019). A multi-parameterized water quality prediction method with differential computing among sampling sites. In T. Endrjukaite, H. Jaakkola, A. Dudko, Y. Kiyoki, B. Thalheim, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXX (pp. 195-207). (Frontiers in Artificial Intelligence and Applications; Vol. 312). IOS Press. https://doi.org/10.3233/978-1-61499-933-1-195