<br>城市雨水湿池除污效率评价及机器学习算法的应用,Science of the Total Environment

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城市雨水湿池除污效率评价及机器学习算法的应用
Science of the Total Environment ( IF 8.2 ) Pub Date : 2023-09-17 , DOI: 10.1016/j.scitotenv.2023.167119
Yang Yang ₁ , David Z Zhu ₂ , Mark R Loewen ₁ , Sherif S Ahmed ₁ , Wenming Zhang ₁ , Haibin Yan ₁ , Bert van Duin ₃ , Khizar Mahmood ₄

Affiliation

湿池已广泛用于控制城市流域的雨水污染物，例如沉积物和营养物质。污染物的去除依赖于物理、化学和生物过程的组合。评估湿池塘的去除效率性能并开发有效的去除效率预测建模方案以优化水质管理至关重要。为了实现这一目标，在加拿大阿尔伯塔省卡尔加里的两个湿池进行了一项为期两年的实地计划，以评估湿池的性能。此外，机器学习（ML）算法已被证明可以在变量之间具有复杂交互的数据集中提供有前途的预测。本研究采用广义线性模型（GLM）、偏最小二乘法（PLS）回归、支持向量机（SVM）、随机森林（RF）和 K 最近邻（KNN）预测总悬浮物（TSS）、总氮（TN）和总磷（TP）三种关键污染物的流出浓度。一般来说，两个研究池塘中流入污染物的浓度变化很大，并且观察到的去除效率范围很广。结果表明，TSS、TN 和 TP 的浓度从池塘的入口到出口显著降低。同时，流入浓度、降雨特性和风是池塘清除效率的重要指标。此外，ML 算法可以成为预测流出水质的有效方法：PLS、GLM 和 SVM 在捕获湿池中的动态相互作用和预测流出浓度方面显示出强大的潜力。这项研究强调了湿池塘中污染物去除动态的复杂性，并展示了数据驱动的流出水质预测的潜力。

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Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms

Wet ponds have been extensively used for controlling stormwater pollutants, such as sediment and nutrients, in urban watersheds. The removal of pollutants relies on a combination of physical, chemical, and biological processes. It is crucial to assess the performance of wet ponds in terms of removal efficiency and develop an effective modeling scheme for removal efficiency prediction to optimize water quality management. To achieve this, a two-year field program was conducted at two wet ponds in Calgary, Alberta, Canada to evaluate the wet ponds' performance. Additionally, machine learning (ML) algorithms have been shown to provide promising predictions in datasets with intricate interactions between variables. In this study, the generalized linear model (GLM), partial least squares (PLS) regression, support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) were applied to predict the outflow concentrations of three key pollutants: total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Generally, the concentrations of inflow pollutants in the two study ponds are highly variable, and a wide range of removal efficiencies are observed. The results indicate that the concentrations of TSS, TN, and TP decrease significantly from the inlet to outlet of the ponds. Meanwhile, inflow concentration, rainfall characteristics, and wind are important indicators of pond removal efficiency. In addition, ML algorithms can be an effective approach for predicting outflow water quality: PLS, GLM, and SVM have shown strong potential to capture the dynamic interactions in wet ponds and predict the outflow concentration. This study highlights the complexity of pollutant removal dynamics in wet ponds and demonstrates the potential of data-driven outflow water quality prediction.

更新日期：2023-09-17

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