长江科学院院报 ›› 2023, Vol. 40 ›› Issue (10): 14-21.DOI: 10.11988/ckyyb.20220718

• 河湖保护与治理 • 上一篇    下一篇

基于时间序列的鄱阳湖Chl-a预测模型优化构建

钱春龙1, 曾一川2, 袁伟皓2, 吴怡2   

  1. 1.江苏中瑞咨询有限公司,南京 210036;
    2.河海大学 环境学院,南京 210098
  • 收稿日期:2022-06-28 修回日期:2022-09-26 出版日期:2023-10-01 发布日期:2023-10-13
  • 通讯作者: 曾一川(1998-),男,四川泸州人,博士研究生,主要从事水环境数值模拟研究。E-mail:zychhu@163.com
  • 作者简介:钱春龙(1972-),男,江苏泰州人,高级工程师,主要从事水环境治理及工业废水治理研究。E-mail:bingjialona@163.com
  • 基金资助:
    国家科技重大专项(2017ZX07203002-01)

Optimal Construction of Chl-a Prediction Model for Poyang Lake Based on Time Series

QIAN Chun-long1, ZENG Yi-chuan2, YUAN Wei-hao2, WU Yi2   

  1. 1. Jiangsu Zhongrui Consulting Co., Ltd., Nanjing 210036, China;
    2. College of Environment, Hohai University, Nanjing 210098, China
  • Received:2022-06-28 Revised:2022-09-26 Online:2023-10-01 Published:2023-10-13

摘要: 为提升鄱阳湖富营养化评估与预测适应性,选择湖区代表性点位2012—2020年的逐月监测作为模型样本训练时间。模型自变量选择水体主要理化参数,并计算其中水质参数部分的综合水质标识指数(WQII)以评价鄱阳湖近年水质演变趋势。进一步地,分别建立响应变量为叶绿素a(Chl-a)的多元线性逐步回归方程(MLSR)、季节性自回归求和移动平均模型(SARIMA),并预测所有点位2020年6—8月份的浓度值,与实测值比较以检验两种模型适用程度。结果显示:各代表监测点的综合水质在2018年出现提升且在丰水期更优,平均WQII排序为康山(2.91)>都昌(3.01)>蚌湖(3.11)>蛇山(3.31);SARIMA模型相比于MLSR方程的Chl-a浓度预测准确度更高,为大型通江湖泊藻类爆发预警提供了优化理论参考。

关键词: 通江湖泊, 综合水质标识指数法, Chl-a, SARIMA, 逐步回归

Abstract: To enhance the adaptability of eutrophication assessment and prediction in Poyang Lake, monthly monitoring data from representative locations within the lake area from 2012 to 2020 were selected as model training samples. Key physicochemical parameters of the lake were selected as independent variables for the model. The water quality integrated index (WQII) was calculated to assess the water quality changes in recent years. A multiple linear stepwise regression equation (MLSR) with Chl-a as response variable, and a seasonal autoregressive summation moving average model (SARIMA) were established respectively. The concentration values of Chl-a from June to August 2020 were predicted and compared with the measured values to assess the applicability of the two models. Results indicated an improvement in the overall water quality of the representative monitoring sites in Poyang Lake in 2018, with better conditions during flood season. The average WQII ranks in an order of Kangshan (2.91), Duchang (3.01), Banghu (3.11), and Shehan (3.31). Moreover, the SARIMA model demonstrated higher accuracy in predicting Chl-a concentrations compared to the MLSR equation, thereby offering an optimized theoretical framework for early warning of algal outbreaks in large river-connected lakes.

Key words: river-connected lake, WQII method, chlorophyll-a, SARIMA, stepwise regression

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