Water Quality Index Analysis and Prediction: A Case Study of Canals in Bangkok Thailand

Petchporn Chawakitchareon, Bernhard Thalheim, Yasushi Kiyoki

研究成果: Conference contribution

抄録

This paper presents a comparison of prediction methods for a water quality index (WQI) that is used for classification of water quality in rivers or canals. In this work, we consider the water quality index of two canals namely Phadung Krung Kasem Canal and Saen Saep Canal, Bangkok, Thailand as a case study. We compare results from M5P, M5Rules, REPTree with results from multilayer perceptron. The models employ five input variables including dissolved oxygen (DO), biological oxygen demand (BOD), ammonia nitrogen (NH3-N), Fecal Coliform bacteria (FCB) and Total Coliform bacteria (TCB) which were measured in the canals. The data in this research had been collected from Bangkok Metropolitan Authority, Thailand from 1 January 2007 to 31 November 2017. The total number of data is 2,000 records. The 10-fold cross validation method is used for evaluation of prediction models. It allows to determine the most effective method. Our experimental results show that the REPTree method yielded the highest accuracy to predict water quality index compared to other methods proposed in this paper.

本文言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXXI
編集者Ajantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
出版社IOS Press
ページ419-429
ページ数11
ISBN(電子版)9781643680446
DOI
出版ステータスPublished - 2019 12 13
イベント29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
継続期間: 2019 6 32019 6 7

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
321
ISSN(印刷版)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
国/地域Finland
CityLappeenranta
Period19/6/319/6/7

ASJC Scopus subject areas

  • 人工知能

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