基于遗传算法的支持向量回归预测生化需氧量

IF 2.4 4区 环境科学与生态学 Q2 WATER RESOURCES Water Quality Research Journal Pub Date : 2023-04-24 DOI:10.2166/wqrj.2023.015
Y. Liu, Zhiyuan Chen
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引用次数: 0

摘要

5天生化需氧量(BOD5)是废水污染强度的重要指标。测定BOD5的过程是在5天的潜伏期内,在20℃条件下,测量1升水中所消耗的分子氧的质量。这是一个耗时的过程,如果测量结果显示水体受到严重污染,水管理机构往往来不及及时作出反应。生物传感器可以简化BOD5的测量过程;然而,测量结果往往与测量的BOD5值有很大的偏差。本研究的主要目的是识别一种机器学习模型,该模型可以从历史数据中预测BOD5值,从而更容易提前发现水污染并及时采取处理措施。本文研究了线性回归、支持向量回归(SVR)和多层感知器(MLP)三种机器学习技术以及两种优化过程。实验中实现了预处理(一次性)、模型训练、模型评价(测试)和分析四个主要步骤。在三种特征选择策略下,实验结果表明,采用遗传算法(GA)优化器的SVR获得了最佳的性能,R2为0.694,MAE最低,为0.109。
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Prediction of biochemical oxygen demand with genetic algorithm-based support vector regression
Five-day biochemical oxygen demand (BOD5) is a vital wastewater contamination strength indicator. The process of measuring BOD5 is to measure the mass of molecular oxygen consumed in 1 L of water at 20 °C over 5-day incubation period. It is a time-consuming process and often too late for water management agencies to make a timely reaction if the result of measurement shows a water body is seriously polluted. Biosensors can simplify the process of BOD5 measurement; however, the measurement results often deviate significantly from the measured BOD5 values. The main aim of this research is to identify a machine learning model, which could predict BOD5 value from historical data and make it easier to detect water pollution in advance and timely adopt treatment measures. Three machine learning techniques, linear regression, support vector regression (SVR) and multi-layer perceptron (MLP) and two optimization processes have been studied in this research. Four main steps, preprocessing (one-time only), model training, model evaluation (testing) and analysis have been implemented in the experiments. With three feature selection strategies, the results of the experiment showed that SVR with genetic algorithm (GA) optimizer achieved the best performance with R2 of 0.694 and the lowest MAE of 0.109.
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CiteScore
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自引率
8.70%
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