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

QIAN Chun-long; ZENG Yi-chuan; YUAN Wei-hao; WU Yi

doi:10.11988/ckyyb.20220718

PDF(6505 KB)

Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (10) : 14-21. DOI: 10.11988/ckyyb.20220718

River-Lake Protection and Regulation

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

Author information +

History +

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

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

QIAN Chun-long, ZENG Yi-chuan, YUAN Wei-hao, WU Yi. Optimal Construction of Chl-a Prediction Model for Poyang Lake Based on Time Series[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(10): 14-21 https://doi.org/10.11988/ckyyb.20220718

References

[1] 刘永, 蒋青松, 梁中耀, 等. 湖泊富营养化响应与流域优化调控决策的模型研究进展[J]. 湖泊科学, 2021, 33(1): 49-63.
[2] CROCKER R, BLAKE W H, HUTCHINSON T H, et al. Spatial Distribution of Sediment Phosphorus in a Ramsar Wetland[J]. Science of the Total Environment, 2021, 765: 142749-142758.
[3] GUYA F J. Biogeochemical Characterization, Phosphorus Sources and Intrinsic Drivers to Its Speciation within the Nyanza Gulf of Lake Victoria[J]. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use, 2020, 25(1): 31-43.
[4] YANG C, KIM D-K, BOWMAN J, et al. Predicting the Likelihood of a Desirable Ecological Regime Shift: A Case Study in Cootes Paradise Marsh, Lake Ontario, Ontario, Canada[J]. Ecological Indicators, 2020, 112: 105794-105808.
[5] DE SOUZA FRAGA M, REIS G B, SILVA DD, et al. Use of Multivariate Statistical Methods to Analyze the Monitoring of Surface Water Quality in the Doce River Basin, Minas Gerais, Brazil[J]. Environmental Science and Pollution Research, 2020, 27(28): 35303-35318.
[6] 郭振天, 黄峰, 郭利丹, 等. 鄱阳湖水文情势演变原因及对策[J].长江科学院院报,2021,38(6):27-31.
[7] 陈炼钢, 陈黎明, 贾建伟, 等. 鄱阳湖枯季水位变化对越冬水鸟生境面积的定量影响[J]. 水利学报, 2019, 50(12): 1502-1509.
[8] 吴芳丽, 胡梦红, 王有基. 长江江豚资源现状及保护对策分析[J]. 人民长江, 2014, 45(增刊1): 40-44.
[9] 刘聚涛, 温春云, 韩柳, 等. 2012—2017年鄱阳湖水位变化与氮磷响应特征研究[J]. 环境污染与防治, 2020, 42(10): 1274-1279.
[10] 赵爽, 倪兆奎, 黄冬凌, 等. 基于WQI法的鄱阳湖水质演变趋势及驱动因素研究[J]. 环境科学学报, 2020, 40(1): 179-187.
[11] 温春云,刘聚涛,胡芳,等.鄱阳湖水质变化特征及水体富营养化评价[J].中国农村水利水电,2020(11): 83-88.
[12] 刘恋, 王国英. 鄱阳湖水环境质量及主要污染物变化趋势分析[J]. 水文, 2016, 36(3): 61-64, 96.
[13] 徐祖信. 我国河流单因子水质标识指数评价方法研究[J]. 同济大学学报(自然科学版), 2005, 33(3): 321-325.
[14] 马明真, 高扬, 宋贤威, 等. 鄱阳湖地区多尺度流域水体重金属输送特征及其污染风险评价[J]. 生态学报, 2019, 39(17): 6404-6415.
[15] 栾华龙, 刘同宦, 杨文俊, 等. 鄱阳湖五河及湖区生态岸坡及滨水带综合治理关键技术体系初探[J].长江科学院院报,2021,38(6):137-142.
[16] 马京久, 喻婷, 陈燕飞, 等. 基于综合水质标识指数法的汉江中下游水质评价[J]. 人民珠江, 2020, 41(9): 63-69.
[17] 王兆群, 张宁红, 张咏. 洪泽湖藻类与环境因子逐步回归统计和蓝藻水华初步预测[J]. 中国环境监测, 2012, 28(4): 17-20.
[18] 游士兵, 严研. 逐步回归分析法及其应用[J]. 统计与决策, 2017(14): 31-35.
[19] LIU H, TIAN H Q, LI Y F. Comparison of Two New ARIMA-ANN and ARIMA-Kalman Hybrid Methods for Wind Speed Prediction[J]. Applied Energy, 2012, 98: 415-424.
[20] 刘春红, 杨亮, 邓河, 等. 基于ARIMA和BP神经网络的猪舍氨气浓度预测[J]. 中国环境科学, 2019, 39(6): 2320-2327.
[21] 赵鹏, 李璐. 基于ARIMA模型的城市轨道交通进站量预测研究[J]. 重庆交通大学学报(自然科学版), 2020, 39(1): 40-44.
[22] BOX G E P, JENKINS G M, REINSEL G C, et al. Time Series Analysis: Forecasting and Control[M].Edition 4. Portland, Oregon: SciTech Book News,2008.
[23] 钱名军, 李引珍, 阿茹娜. 基于季节分解和SARIMA-GARCH模型的铁路月度客运量预测方法[J]. 铁道学报, 2020, 42(6): 25-34.
[24] MER FARUK D. A Hybrid Neural Network and ARIMA Model for Water Quality Time Series Prediction[J]. Engineering Applications of Artificial Intelligence, 2010, 23(4): 586-594.
[25] AKAIKE H. A New Look at the Statistical Model Identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716-723.
[26] 孙惠玲, 廖泽波, 段立曾, 等. 基于空间插值算法的阳宗海夏季水质参数空间分布规律研究[J].长江科学院院报,2017,34(3):30-34.
[27] 连心桥,朱广伟,杨文斌,等.土地利用对太湖入流河道营养盐的影响[J]. 环境科学,2021,42(10):4698-4707.
[28] 胡开明, 王水, 逄勇. 太湖不同湖区底泥悬浮沉降规律研究及内源释放量估算[J]. 湖泊科学, 2014, 26(2): 191-199.
[29] 赵海超,王圣瑞,赵明,等.洱海水体溶解氧及其与环境因子的关系[J].环境科学,2011,32(7):1952-1959.
[30] 钱昊钟, 赵巧华, 钱培东, 等. 太湖叶绿素a浓度分布的时空特征及其影响因素[J]. 环境化学, 2013, 32(5): 789-796.
[31] 蒋定国, 全秀峰, 李飞, 等. 基于BP神经网络的水体叶绿素a浓度预测模型优化研究[J]. 南水北调与水利科技, 2019, 17(2): 81-88.
[32] 欧阳添, 闪锟, 周博天, 等. 基于LSTM网络的在线藻类时序数据预测研究: 以三峡水库为例[J]. 湖泊科学, 2021, 33(4): 1031-1042.
[33] 史代敏, 谢小燕. 应用时间序列分析[M].北京:高等教育出版社, 2011.
[34] KHODAKHAH H, AGHELPOUR P, HAMEDI Z. Comparing Linear and Non-linear Data-driven Approaches in Monthly River Flow Prediction, Based on the Models SARIMA, LSSVM, ANFIS, and GMDH[J]. Environmental Science and Pollution Research, 2022, 29(15): 21935-21954.
[35] CHEN M, LI J, DAI X, et al. Effect of Phosphorus and Temperature on Chlorophyll-a Contents and Cell Sizes of Scenedesmus Obliquus and Microcystis Aeruginosa[J]. Limnology, 2011, 12(2): 187-192.
[36] 袁伟皓, 王华, 曾一川, 等. 大型通江湖泊藻类增殖驱动要素的时空分异特征[J]. 环境工程, 2021, 39(10): 64-71, 128.