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采用改进的神经模糊方法和海洋捕食者算法对生化需氧量进行建模。

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.

机构信息

School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.

Department of Civil Engineering, Lübeck University of Applied Science, 23562, Lubeck, Germany.

出版信息

Environ Sci Pollut Res Int. 2023 Sep;30(41):94312-94333. doi: 10.1007/s11356-023-28935-6. Epub 2023 Aug 2.

DOI:10.1007/s11356-023-28935-6
PMID:37531049
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10468928/
Abstract

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.

摘要

生化需氧量 (BOD) 是水质评估中最重要的参数之一。替代方法对于准确预测该参数至关重要，因为传统的 BOD 预测方法耗时且由于微生物多样性的不稳定性而不准确。在这项研究中，研究了四种混合神经模糊 (ANFIS) 方法的适用性，包括遗传算法 (GA) 的 ANFIS、粒子群优化 (PSO) 的 ANFIS、正弦余弦算法 (SCA) 的 ANFIS 和海洋捕食者算法 (MPA) 的 ANFIS，这些方法用于预测 BOD，使用了不同的输入组合，如来自韩国 Gongreung 和 Gyeongan 两个河流站的氢离子潜力 (pH)、溶解氧 (DO)、电导率 (EC)、水温度 (WT)、悬浮固体 (SS)、化学需氧量 (COD)、总氮 (TN) 和总磷 (T-P)。还检查了多元自适应回归样条 (MARS) 在确定最佳输入组合方面的适用性。结果发现，ANFIS-MPA 是最好的模型，具有最低的均方根误差和平均绝对误差以及最高的确定系数。与 Gongreung 站的 ANFIS-PSO、ANFIS-GA 和 ANFIS-SCA 模型相比，它在测试阶段分别提高了 13.8%、12.1%和 6.3%，在 Gyeongan 站分别提高了 33%、25%和 6.3%。

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