Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China
Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China
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DOI：
10.1007/s11356-022-22758-7
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发表时间：
2022-08-30
影响因子：
5.8
通讯作者：
Wang, Yonggui
中科院分区：
文献类型：
--
作者：
Li, Qiang;Yang, Yinqun;Wang, Yonggui
海河流域是我国水资源最为短缺和水污染最为严重的地区，其水质预测对水资源管理具有重要意义。长短期记忆（LSTM）是近年来广泛应用于水质预测的工具。LSTM用于不同指标的水质预测的性能和适应性需要在特定流域采用之前进行讨论。然而，文献中对不同水质指标的各种预测精度及其原因的对比分析研究甚少，尤其是在海河流域。采用LSTM预测生化需氧量（BOD）、高锰酸盐指数（CODMn）、溶解氧（DO）、氨氮（NH3-N）、总磷（TP）、氢离子浓度（pH）和重铬酸钾消化的化学需氧量（CODCr）。结果表明，LSTM对BOD、CODMn、CODCr和TP的Nash-Sutcliffe效率中值分别为0.766、0.835、0.837和0.711，对NH3-N、DO和pH的Nash-Sutcliffe效率中值分别为0.638、0.625和0.229，LSTM对这3个指标的预测效果较好。此外，LSTM预测水质的性能与最大信息系数计算的水质指标的时间自相关系数和互相关系数的最大值呈线性关系，决定系数为0.79至约0.80。该研究将为LSTM在水质预测中的实际应用和改进提供新的知识和支持。
As the most water shortage and water polluted area in China, the water quality prediction is of utmost needed and important in Haihe River Basin for its water resource management. The long short-term memory (LSTM) has been a widely used tool for water quality forecast in recent years. The performance and adaptability of LSTM for water quality prediction of different indicators needs to be discussed before it adopted in a specific basin. However, literature contains very few studies on the comparative analysis of the various prediction accuracy of different water quality indicators and the causes, especially in Haihe River Basin. In this study, LSTM was employed to predict biochemical oxygen demand (BOD), permanganate index (CODMn), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), hydrogen ion concentration (pH), and chemical oxygen demand digested by potassium dichromate (CODCr). According to results under 24 different input conditions, it is demonstrated that LSTMs present better predicting on BOD, CODMn, CODCr, and TP (median Nash-Sutcliffe efficiency reaching 0.766, 0.835, 0.837, and 0.711, respectively) than NH3-N, DO, and pH (median Nash-Sutcliffe efficiency of 0.638, 0.625, and 0.229, respectively). Besides, the performance of LSTM to predict water quality is linearly related to the maximum value of temporal autocorrelation and cross-correlation coefficients of water quality indicators calculated by maximal information coefficient with the coefficients of determination of 0.79 to approximately 0.80. This study would provide new knowledge and support for the practical application and improvement of the LSTM in water quality prediction.