基于黏菌算法的梯度增强决策树在污水处理系统中的应用

IF 4.3 4区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL Water Reuse Pub Date : 2023-07-20 DOI:10.2166/wrd.2023.046
Jyoti Chauhan, R. M. Rani, V. Prashanthi, H. Almujibah, A. Alshahri, Koppula Srinivas Rao, A. Radhakrishnan
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引用次数: 0

摘要

改善污水处理系统的基础设施、运营、监控、维护和管理的一种方法是使用机器学习建模来制作智能预测、跟踪和故障预测系统。该方法旨在利用工业数据对废水处理模型进行处理。逐步采用梯度增强决策树(GBDT)算法对污水厂参数进行预测。此外,我们使用黏菌算法(SMA)进行特征提取和其他可接受的调谐程序。污水处理系统的输入和流出化学需氧量(COD)预测适用于本研究中采用的GBDT方法。GBDT-SMA采用人工智能为复杂系统提供精确的方法建模。使用了几种训练和模型测试技术来确定神经网络模型和决策树的最佳拓扑。GBDT-SMA模型在所有方法中表现最好。在500个数据的情况下,GBDT-SMA的准确率达到96.32%,优于人工神经网络(ANN)、卷积神经网络(CNN)、深度卷积神经网络(DCNN)和k近邻RF等模型,后者的准确率分别为82.97、87.45、85.98和91.45%。
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Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems
One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.
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来源期刊
Water Reuse
Water Reuse Multiple-
CiteScore
6.20
自引率
8.90%
发文量
0
审稿时长
7 weeks
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