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预测废水处理中的化学氧气需求的混合建模技术:使用神经网络的堆叠合体学习方法

Ramya S¹, Srinath S², Pushpa Tuppad³

¹Department of Computer Science & Engineering, JSS Science and Technology University, Mysuru, India. ramya.shivanagu@gmail.com.

Environmental monitoring and assessment

|November 27, 2024

概括

人工神经网络 (ANN) 通过生成对抗网络 (GAN) 进行数据增强,有效地预测废水的化学氧气需求 (COD). 通过群优化 (ACO) 和粒子群优化 (PSO) 实现了近乎完美的预测准确性.

科学领域:

环境工程环境工程
计算科学计算科学
数据科学数据科学数据科学

背景情况:

废水处理厂 (WWTPs) 需要有效的运行监测,以确保符合环境要求.
预测废水参数,如化学氧气需求 (COD),对于运营效率至关重要.
传统的参数分析实验室方法耗时且劳动密集.

研究的目的:

开发一个强大的人工神经网络 (ANN) 模型,用于预测经过处理的废水中的化学氧需求 (COD).
通过使用生成对抗网络 (GAN) 的数据增强来提高ANN模型性能.
评估各种ANN扩展和回归建模优化技术的有效性.

主要方法:

利用来自城市WWTP的527个样本和22个特征的数据集,使用GANs增加到1054个样本.
应用K-means集群和主要组件分析 (PCA) 用于特征选择和异常检测.
整合了先进的ANN架构 (多项式,添加式,辐射基础网络),并通过遗传算法 (GA),群优化 (ACO) 和粒子群优化 (PSO) 优化它们.

主要成果:

堆叠组合ANN模型实现了0.95的确定系数 (R2) 和0.0012.00的平均平方误差 (MSE).
使用ACO (ACO_ANN) 和PSO (PSO_ANN) 的优化ANN模型表现出卓越的性能,R2分数分别约为0.98和0.99.
对GA优化的ANN模型 (GA_ANN) 的R2得分较低,为0.70,这表明与ACO_ANN和PSO_ANN相比还有改进的余地.

结论:

ANN模型,特别是当通过 ACO 和 PSO 等元启发算法进行优化时,为预测废水废水参数的传统方法提供了强大而准确的替代方案.
使用GAN进行数据增强可以显著提高基于ANN的预测模型的性能和稳定性.
拟议的ANN堆叠组合回归框架为提高WWTP的运营效率和环境合规性提供了有价值的工具.

关键词:

人工神经网络的人工神经网络化学氧气需求化学氧气需求生成性的对抗性网络.优化优化优化优化堆叠集团回归的回归方法废水处理厂的废水处理厂.

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