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

Khoumkham Ladsavong, Petchporn Chawakitchareon, Yasushi Kiyoki

研究成果: Conference contribution

抄録

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, NH 3 -N, NO 2 -N, NO 3 -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.

本文言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXX
編集者Tatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
出版社IOS Press
ページ195-207
ページ数13
ISBN(電子版)9781614999324
DOI
出版ステータスPublished - 2019

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
312
ISSN(印刷版)0922-6389

ASJC Scopus subject areas

  • 人工知能

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