Chengdu University of Information Technology, Chengdu, 610225, China; Department of Civil Engineering, McMaster University, Hamilton, Ontario, L8S 4L8, Canada.
Department of Civil Engineering, McMaster University, Hamilton, Ontario, L8S 4L8, Canada.
Environ Pollut. 2021 Dec 15;291:118116. doi: 10.1016/j.envpol.2021.118116. Epub 2021 Sep 7.
Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.
实时河氯预测因其在氯控制和管理中的重要性而受到广泛关注。本研究开发了一种人工神经网络模型（即多层感知器，MLP）和统计推断模型（即逐步聚类分析，SCA），用于预测河水中的氯浓度。然后，提出了一种基于 MLP 和 SCA 的集成学习模型，以进一步提高建模精度。通过加拿大格兰德河的小时河氯预测案例研究，演示了模型的适用性。结果表明，与两个集成成员相比，所提出的集成学习模型 MLP-SCA 在 RMSE、MAPE、NSE 和 R 方面具有最佳的整体性能，其值分别为 11.58mg/L、27.55%、0.90 和 0.90。此外，MLP-SCA 更擅长预测极高的氯浓度。预测观测浓度高于 150mg/L 的 RMSE 和 MAPE 值分别为 9.88mg/L 和 4.40%。所提出的 MLP-SCA 的出色性能，特别是在极值预测方面，表明它可以使用常用数据（即电导率、水温、河流流量和降雨量）提供可靠的氯预测。格兰德河氯浓度的高频预测可以补充现有的水质监测计划，并进一步支持流域中氯的实时控制和管理。MLP-SCA 是第一个用于河氯预测的集成学习模型，可扩展到其他河流系统进行水质预测。
