Shang Manting, Huang Jiaao, Liu Peigui, Gao Jingjing, Li Jiaxuan
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
College of Civil Engineering, Hefei University of Technology, Hefei 230009, China E-mail:
Water Sci Technol. 2025 Apr;91(8):960-976. doi: 10.2166/wst.2025.051. Epub 2025 Apr 9.
To address the issue of poor prediction accuracy and performance caused by the influence of the original data sequence on the first-order single-variable gray model (GM(1,1)), this study proposes an exponential smoothing gray model (ESGM(1,1)). Taking the Anliu Station situated at the border between Henan and Anhui provinces as an example, ammonia nitrogen and the permanganate index were selected for water quality prediction using the GM(1,1) and ESGM(1,1) models from 2010 to 2021. The fitting accuracy of these models is evaluated by comparing the computed values with the actual monitored water quality index values. The results reveal that the average relative percentage error in the simulation period decreased by 3.01% compared with GM(1,1) and further decreased by 27.41% during the verification period. The mean square error ratio of GM(1,1) was 0.79, which failed the fitting accuracy test. The value of ESGM(1,1) was 0.59, which successfully passed the test. The predicted results were consistent with the monitoring data from 2010 to 2021. It is concluded that ESGM(1,1) shows superior accuracy for short-term water quality prediction. This model mitigates the impact of the initial sequence on prediction accuracy and can be utilized for local water pollution control and environmental protection.
为解决原始数据序列对一阶单变量灰色模型（GM(1,1)）的影响所导致的预测准确性和性能不佳问题，本研究提出了一种指数平滑灰色模型（ESGM(1,1)）。以位于河南和安徽交界处的安溜站为例，选取氨氮和高锰酸盐指数，利用GM(1,1)和ESGM(1,1)模型对2010年至2021年的水质进行预测。通过将计算值与实际监测的水质指标值进行比较，评估这些模型的拟合精度。结果表明，与GM(1,1)相比，模拟期内平均相对百分比误差降低了3.01%，验证期内进一步降低了27.41%。GM(1,1)的均方误差比为0.79，未通过拟合精度检验。ESGM(1,1)的值为0.59，成功通过检验。预测结果与2010年至2021年的监测数据一致。得出结论，ESGM(1,1)在短期水质预测中表现出卓越的准确性。该模型减轻了初始序列对预测准确性的影响，可用于当地水污染控制和环境保护。
