模拟生物化学氧气需求,使用海洋捕食者算法改进的神经模糊方法.
Rana Muhammad Adnan1, Hong-Liang Dai1, Ozgur Kisi2,3
1School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.
Environmental science and pollution research international
|August 2, 2023
概括
准确的水质评估需要改进生化氧需求 (BOD) 预测. 混合ANFIS-MPA模型为预测BOD水平的传统方法提供了更优越,更快的替代方案.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 计算智能是一种计算智能.
背景情况:
- 生物化学氧气需求 (BOD) 是一个关键的水质指标.
- 传统的BOD测量是耗时且容易出现不准确的.
- 开发准确的预测模型对于高效的水质评估至关重要.
研究的目的:
- 评估混合适应性神经模糊推理系统 (ANFIS) 模型用于预测BOD.
- 为了比较ANFIS与遗传算法 (GA),粒子集群优化 (PSO),正弦正弦算法 (SCA) 和海洋捕食者算法 (MPA) 集成的性能.
- 用多变量自适应回归线 (MARS) 来确定BOD预测的最佳输入组合.
主要方法:
- 使用了四种混合ANFIS模型:ANFIS-GA,ANFIS-PSO,ANFIS-SCA,以及ANFIS-MPA. 这四种混合ANFIS模型都在使用中.
- 输入变量包括pH,溶氧 (DO),电导率 (EC),水温 (WT),悬浮固体 (SS),化学氧气需求 (COD),总 (TN) 和总 (T-P).
- 应用MARS来确定用于BOD预测的最有效的输入参数.
主要成果:
- 在BOD预测方面,ANFIS-MPA模型表现最好.
- 安菲斯-MPA实现了最低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE),以及最高的确定系数 (R2).
- 与其他模型相比,ANFIS-MPA显著改善了RMSE,特别是在基昂冈站 (提高了33%).
结论:
- 混合ANFIS模型,特别是ANFIS-MPA,提供了一个非常准确和高效的方法来预测BOD.
- 这些发现表明ANFIS-MPA是传统BOD测试的可行替代品,用于评估水质.
- 使用MARS优化输入变量可以提高开发模型的预测准确性.
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