生化需氧量(BOD)是用于水质评价的最重要参数之一。替代方法对于准确预测此参数至关重要,因为预测BOD的传统方法耗时且由于微生物多重性的不一致而不准确。在这项研究中,四种混合神经模糊(ANFIS)方法的适用性,采用遗传算法(GA)的ANFIS,采用粒子群优化(PSO)的ANFIS,采用正弦余弦算法(SCA)的ANFIS,和ANFIS与海洋捕食者算法(MPA),在使用不同的输入组合预测BOD时进行了研究,如氢电位(pH),溶解氧(DO),电导率(EC),水温(WT),悬浮固体(SS),化学需氧量(COD),总氮(TN),和从两个河流站获得的总磷(T-P),贡荣和庆根,韩国。研究了多元自适应回归样条(MARS)在确定最佳输入组合中的适用性。发现ANFIS-MPA是最佳模型,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。改进了ANFIS-PSO的均方根误差,ANFIS-GA,ANFIS-SCA模型下降了13.8%,12.1%,而井场站则为6.3%,下降33%,25%,测试阶段的庆安站为6.3%,分别。
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.