The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

中文翻译：

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土耳其东部黑海盆地溶解氧浓度的空间预测

本研究的目的是模拟、监测和评估土耳其东部黑海 (EBS) 流域的地表水质量。七个溪流每月监测的水质指标是水温 (WT)、pH、总溶解固体 (TDS) 和电导率 (EC)，以及发光溶解氧 (LDO) 浓度和饱和度。基于 18 个月的数据监测，参照土耳其地表水质法规对地表水质变化进行了空间和时间评估。首先，将基于教学的优化 (TLBO) 算法和传统回归分析 (CRA) 应用于三种不同的回归形式，即指数、幂和线性函数，以预测 LDO 浓度。然后，采用多元自适应回归样条 (MARS) 方法，并使用平均绝对误差 (MAE)、均方根误差 (RMSE) 和 Nash Sutcliffe 效率系数 (NSCE) 三个性能指标来评估性能。 MARS、TLBO 和 CRA 方法。监测结果显示，所有河流都表现出相同的趋势，即冬季较低的 WT 值导致 LDO 浓度较高，而夏季较高的 WT 值导致 LDO 浓度较低。同样，秋季，TDS 浓度较高，EC 值较高，而春季，TDS 浓度较低，EC 值较低。得出的结论是，就原位监测的参数而言，EBS 流域内河流的水质为优质水，其中 LDO 浓度夏季为 9.13~10.12 mg/L，冬季为 12.31~13.26 mg/L。当比较三种模型的预测精度时，可以看出 MARS 方法提供了比其他方法更成功的结果。TLBO 和 CRA 方法的结果彼此非常接近。对于最佳 MARS 模型，RMSE、MAE 和 NSCE 值分别为 0.2599 mg/L、0.2125 mg/L 和 0.9645，而对于最佳 MARS 模型，这些值分别为 0.4167 mg/L、0.3068 mg/L 和 0.9086。最好的 TLBO 和 CRA 模型。一般来说，LDO 浓度可以使用 MARS 方法与 WT、EC 和 pH 变量的各种输入组合成功预测。当比较三种模型的预测精度时，可以看出 MARS 方法提供了比其他方法更成功的结果。TLBO 和 CRA 方法的结果彼此非常接近。对于最佳 MARS 模型，RMSE、MAE 和 NSCE 值分别为 0.2599 mg/L、0.2125 mg/L 和 0.9645，而对于最佳 MARS 模型，这些值分别为 0.4167 mg/L、0.3068 mg/L 和 0.9086最好的 TLBO 和 CRA 模型。一般来说，LDO 浓度可以使用 MARS 方法与 WT、EC 和 pH 变量的各种输入组合成功预测。当比较三种模型的预测精度时，可以看出 MARS 方法提供了比其他方法更成功的结果。TLBO 和 CRA 方法的结果彼此非常接近。对于最佳 MARS 模型，RMSE、MAE 和 NSCE 值分别为 0.2599 mg/L、0.2125 mg/L 和 0.9645，而对于最佳 MARS 模型，这些值分别为 0.4167 mg/L、0.3068 mg/L 和 0.9086。最好的 TLBO 和 CRA 模型。一般来说，LDO 浓度可以使用 MARS 方法与 WT、EC 和 pH 变量的各种输入组合成功预测。对于最佳 MARS 模型，分别为 9645，而对于最佳 TLBO 和 CRA 模型，这些值分别为 0.4167 mg/L、0.3068 mg/L 和 0.9086。一般来说，LDO 浓度可以使用 MARS 方法与 WT、EC 和 pH 变量的各种输入组合成功预测。对于最佳 MARS 模型，分别为 9645，而对于最佳 TLBO 和 CRA 模型，这些值分别为 0.4167 mg/L、0.3068 mg/L 和 0.9086。一般来说，LDO 浓度可以使用 MARS 方法与 WT、EC 和 pH 变量的各种输入组合成功预测。