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

Khoumkham Ladsavong; Petchporn Chawakitchareon; Yasushi Kiyoki

doi:10.3233/978-1-61499-933-1-195

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

Khoumkham Ladsavong, Petchporn Chawakitchareon, Yasushi Kiyoki

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Information Modelling and Knowledge Bases XXX
Editors	Tatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
Publisher	IOS Press
Pages	195-207
Number of pages	13
ISBN (Electronic)	9781614999324
DOIs	https://doi.org/10.3233/978-1-61499-933-1-195
Publication status	Published - 2019 Jan 1

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	312
ISSN (Print)	0922-6389

Keywords

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

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3233/978-1-61499-933-1-195

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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

A multi-parameterized water quality prediction method with differential computing among sampling sites. / Ladsavong, Khoumkham; Chawakitchareon, Petchporn; Kiyoki, Yasushi.

Information Modelling and Knowledge Bases XXX. ed. / Tatiana Endrjukaite; Hannu Jaakkola; Alexander Dudko; Yasushi Kiyoki; Bernhard Thalheim; Naofumi Yoshida. IOS Press, 2019. p. 195-207 (Frontiers in Artificial Intelligence and Applications; Vol. 312).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Frontiers in Artificial Intelligence and Applications, vol. 312, IOS Press, pp. 195-207. https://doi.org/10.3233/978-1-61499-933-1-195

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

Ladsavong, Khoumkham ; Chawakitchareon, Petchporn ; Kiyoki, Yasushi. / A multi-parameterized water quality prediction method with differential computing among sampling sites. Information Modelling and Knowledge Bases XXX. editor / Tatiana Endrjukaite ; Hannu Jaakkola ; Alexander Dudko ; Yasushi Kiyoki ; Bernhard Thalheim ; Naofumi Yoshida. IOS Press, 2019. pp. 195-207 (Frontiers in Artificial Intelligence and Applications).

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