TY - GEN
T1 - Application of data mining software to predict the alum dosage in coagulation process
T2 - 27th International Conference on Information Modelling and Knowledge Bases, EJC 2017
AU - Ladsavong, Khoumkham
AU - Chawakitchareon, Petchporn
AU - Kiyoki, Yasushi
N1 - Funding Information:
This work is supported by AUN/Seed-net Collaborative research scholarship and Chulalongkorn University. This research is also in part support by GESL program, Keio University, Japan.
Publisher Copyright:
© 2018 The authors and IOS Press. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Alum dosage
KW - Coagulation process
KW - Data mining software
KW - Dongmarkkaiy water treatment plant
KW - Lao PDR
KW - Vientiane
KW - Weka
UR - http://www.scopus.com/inward/record.url?scp=85063395964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063395964&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-834-1-110
DO - 10.3233/978-1-61499-834-1-110
M3 - Conference contribution
AN - SCOPUS:85063395964
T3 - Frontiers in Artificial Intelligence and Applications
SP - 110
EP - 124
BT - Information Modelling and Knowledge Bases XXIX
A2 - Sornlertlamvanich, Virach
A2 - Koopipat, Chawan
A2 - Kiyoki, Yasushi
A2 - Thalheim, Bernhard
A2 - Hansuebsai, Aran
A2 - Yoshida, Naofumi
A2 - Chawakitchareon, Petchporn
A2 - Jaakkola, Hannu
PB - IOS Press
Y2 - 5 June 2017 through 9 June 2017
ER -