Zhang Yue, Hou Jun, Gu Yuwei, Zhu Xingyu, Xia Jun, Wu Jun, You Guoxiang, Yang Zijun, Ding Wei, Miao Lingzhan
Key Laboratory of Integrated Regulation and Resources Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China.
Jiangsu Province Water Resources Planning Bureau, Nanjing, 210029, China.
Environ Manage. 2025 Mar;75(3):694-709. doi: 10.1007/s00267-024-02108-8.
Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North Water Diversion Project, Lakes Hongze and Luoma play a key role in water resource management, making the prediction of cyanobacterial blooms in these lakes particularly important. To address this, satellite remote sensing data were utilized to analyze the spatiotemporal dynamics of cyanobacterial blooms in these lakes. Subsequently, a precise machine learning model, integrating the Projection Pursuit Model and Random Forest (PP-RF) algorithms, was developed to predict the extent of cyanobacterial blooms, considering a range of influencing factors, including physical, chemical, climatic, and hydrologic variables. The findings indicated pronounced seasonal fluctuations in cyanobacterial blooms, with higher levels in summer than in other seasons. Key determinants for cyanobacterial blooms prediction included solar radiation, temperature and total nitrogen for Lake Hongze, while for Lake Luoma, significant predictors were identified as temperature, water temperature, and solar radiation. Compared with traditional data preprocessing methods, PP-RF model has advantages in addressing multicollinearity. This study provides a feasible method for predicting cyanobacterial blooms in impounded lakes within inter-basin water transfer projects. By inputting region-specific data, this model could be applied broadly, contributing to against the adverse effects of cyanobacterial blooms and provide scientific guidance for the protection and management of aquatic ecosystems.
浅水湖泊中的蓝藻水华对全球水生生态系统和公众健康构成了重大威胁，凸显了对先进预测方法的迫切需求。作为南水北调东线工程沿线的蓄水湖泊，洪泽湖和骆马湖在水资源管理中发挥着关键作用，因此预测这些湖泊中的蓝藻水华尤为重要。为解决这一问题，利用卫星遥感数据分析了这些湖泊中蓝藻水华的时空动态。随后，开发了一种精确的机器学习模型，该模型整合了投影寻踪模型和随机森林（PP-RF）算法，在考虑包括物理、化学、气候和水文变量等一系列影响因素的情况下，预测蓝藻水华的程度。研究结果表明，蓝藻水华存在明显的季节性波动，夏季的蓝藻水华水平高于其他季节。洪泽湖蓝藻水华预测的关键决定因素包括太阳辐射、温度和总氮，而骆马湖的重要预测因子则为温度、水温及太阳辐射。与传统数据预处理方法相比，PP-RF模型在处理多重共线性方面具有优势。本研究为跨流域调水工程中蓄水湖泊蓝藻水华的预测提供了一种可行方法。通过输入特定区域的数据，该模型可得到广泛应用，有助于对抗蓝藻水华的不利影响，并为水生生态系统的保护和管理提供科学指导。
