Application of data mining software to predict the alum dosage in coagulation process: A case study of Vientaine, Lao PDR

Khoumkham Ladsavong, Petchporn Chawakitchareon, Yasushi Kiyoki

研究成果: Conference contribution

抄録

This paper presents an alum dosage prediction in coagulation process by using Weka Data Mining Software. The data in this research had been collected from Dongmarkkaiy Water Treatment Plant (DWTP), Vientiane capital, Laos PDR from 1st January 2008 to 31st October 2016. The total number of collected data were 2,891 records. In this research, we compared the results from multilayer perceptron (MLP), M5Rules, M5P, and REPTree method by using the root mean square error (RMSE) and mean absolute error (MAE) value. Three input independent variables, i.e. turbidity, pH, and alkalinity were used. The dependent variable was alum added for the coagulation process. Our experimental results indicated that the MLP method yielded the highest precision method in order to predict the alum dosage.

本文言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXIX
編集者Virach Sornlertlamvanich, Chawan Koopipat, Yasushi Kiyoki, Bernhard Thalheim, Aran Hansuebsai, Naofumi Yoshida, Petchporn Chawakitchareon, Hannu Jaakkola
出版社IOS Press
ページ110-124
ページ数15
ISBN(電子版)9781614998334
DOI
出版ステータスPublished - 2018
イベント27th International Conference on Information Modelling and Knowledge Bases, EJC 2017 - Krabi, Thailand
継続期間: 2017 6月 52017 6月 9

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
301
ISSN(印刷版)0922-6389

Conference

Conference27th International Conference on Information Modelling and Knowledge Bases, EJC 2017
国/地域Thailand
CityKrabi
Period17/6/517/6/9

ASJC Scopus subject areas

  • 人工知能

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