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

机译:水质指标分析与预测 - 以曼谷泰国运河为例

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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 (NH_3-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.
机译:本文介绍了水质指数(WQI)预测方法的比较,用于河流或运河中的水质分类。在这项工作中,我们考虑了两个运河的水质指数,即Phadung Krung Kasem Canal和Saen Saep Canal,曼谷,泰国作为案例研究。我们将M5P,M5Rules,Reptree的结果与Multidayer Perceptron的结果进行比较。该模型采用五种输入变量,包括在运河中测量的溶解氧(DO),生物需氧量(BOD),氨氮(NH_3-N),氨氮(NH_3-N),氨氮(NH_3-N),粪便大肠杆菌(TCB)。本研究中的数据从2007年1月1日至2017年11月31日起,泰国曼谷大都会权威。数据总数为2,000条记录。 10倍交叉验证方法用于评估预测模型。它允许确定最有效的方法。我们的实验结果表明,与本文提出的其他方法相比,预测方法得到了预测水质指数的最高精度。

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