Lu Hao, Yang Liuyan, Fan Yifan, Qian Xin, Liu Tong
State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.
State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.
Environ Res. 2022 Mar;204(Pt B):111940. doi: 10.1016/j.envres.2021.111940. Epub 2021 Sep 30.
This study demonstrates the utility of internal nutrient loads as an additional parameter to improve the performance of machine learning models in predicting the temporal variations of aqueous TN and TP concentrations in Taihu Lake, a large shallow lake. Internal loads, as a potential input parameter for machine learning models, were estimated using a mass balance calculation. The results showed that between 2011 and 2018 the maximum monthly internal loads of nitrogen and phosphorus in Taihu Lake were 4200 t and 178 t, respectively. Monthly changes in the aqueous TN and TP concentrations of Taihu Lake did not correlate significantly with inflow loads whereas the correlations with estimated internal loads were positive and significant. Long short-term memory (LSTM), random forest (RF), and gradient boosting regression tree (GBRT) models were built, and for all of them the inclusion of internal loads in the input parameters improved their performance. LSTM model III, whose input parameters included both inflow loads and internal loads, had the best performance, based on a testing root mean square error of 0.11 mg TN/L and 0.017 mg TP/L. A 28 % decrease in the annual aqueous TP concentration in Taihu Lake in 2018 simulated by LSTM model III was achieved by lowering the average water level from 3.29 m to 2.99 m, suggesting a possible strategy to control the TP concentration in the lake. In summary, our study showed that aqueous TN and TP concentrations in shallow lakes can be simulated using machine learning, with LSTM models outperforming RF and GBRT models; in these models, internal loads should be included as an input parameter. Additionally, our study identified the water level as an important factor affecting the aqueous TP concentration in Taihu Lake.
本研究证明了内部营养负荷作为一个附加参数的效用，可用于提高机器学习模型预测大型浅水湖泊太湖水中总氮（TN）和总磷（TP）浓度时间变化的性能。内部负荷作为机器学习模型的一个潜在输入参数，通过质量平衡计算进行估算。结果表明，2011年至2018年期间，太湖氮和磷的最大月内部负荷分别为4200吨和178吨。太湖水中TN和TP浓度的月变化与入流负荷没有显著相关性，而与估算的内部负荷呈正相关且显著。构建了长短期记忆（LSTM）、随机森林（RF）和梯度提升回归树（GBRT）模型，对于所有这些模型，在输入参数中纳入内部负荷均提高了它们的性能。基于测试均方根误差为0.11mg TN/L和0.017mg TP/L，输入参数包括入流负荷和内部负荷的LSTM模型III表现最佳。通过将平均水位从3.29米降至2.99米，LSTM模型III模拟出2018年太湖年水中TP浓度下降了28%，这表明了一种控制湖泊中TP浓度的可能策略。总之，我们的研究表明，利用机器学习可以模拟浅水湖泊中的水中TN和TP浓度，LSTM模型优于RF和GBRT模型；在这些模型中，应将内部负荷作为输入参数纳入。此外，我们的研究确定水位是影响太湖水中TP浓度的一个重要因素。
