Su Jie, Lin Ziyu, Xu Fengwei, Fathi Gholamreza, Alnowibet Khalid A
Basin Research Center for Water Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Sci Rep. 2024 Oct 13;14(1):23927. doi: 10.1038/s41598-024-74144-7.
Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken to control the amount of pollutants and bring them to the allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns in modeling water quality components. Also, in modeling using the MLP (Multilayer Perceptrons) model, both linear and nonlinear pattern are not controlled equally. Therefore, in the present study, linear time series models (ARIMA), MLP model, and a hybrid model of MLP and ARIMA optimized by a Grasshopper optimization algorithm are used to predict water quality components in the statistical period of 2011-2019. In the proposed hybrid method, the ability of the ARIMA and the MLP model are exploited. Observational water quality data for forecasting time series in the hybrid method include dissolved oxygen, water temperature, and boron over 108 months. Since, the hybrid model is capable of realizing the nonlinear essence of complicated time series, it makes more reliable forecasts. In the hybrid model, the correlation coefficients between the observational data and the predicted values are 0.9 for dissolved oxygen, 0.91 for water temperature, and 0.91 for boron. To compare the three ARIMA, MLP, and hybrid models, the accuracy indices of each model are calculated. The results show that the hybrid model's higher accuracy compared with the other two models.
为了对河流流域进行合理管理，以便采取措施控制污染物数量并使其达到允许水平，有必要对河流进行水质监测。自回归积分移动平均（ARIMA）模型在对水质成分进行建模时未考虑非线性模式。此外，在使用多层感知器（MLP）模型进行建模时，线性和非线性模式并未得到同等控制。因此，在本研究中，使用线性时间序列模型（ARIMA）、MLP模型以及通过蚱蜢优化算法优化的MLP与ARIMA混合模型，来预测2011 - 2019统计期内的水质成分。在所提出的混合方法中，利用了ARIMA和MLP模型的能力。混合方法中用于预测时间序列的观测水质数据包括溶解氧、水温以及108个月期间的硼含量。由于混合模型能够认识到复杂时间序列的非线性本质，所以能做出更可靠的预测。在混合模型中，观测数据与预测值之间的相关系数对于溶解氧为0.9，对于水温为0.91，对于硼为0.91。为了比较ARIMA、MLP和混合这三种模型，计算了每个模型的准确性指标。结果表明，混合模型的准确性高于其他两个模型。
