| Citation: |
ZHANG M D,XU Q,LIU Z H,et al.Prediction of water quality time series based on the dynamic sliding window BP neural network model[J].Journal of Environmental Engineering Technology，2022，12（3）：809-815. DOI: 10.12153/j.issn.1674-991X.20210194
|
In order to improve the prediction precision of water quality having time series properties by BP neural network (BPNN), principal component analysis (PCA) was used for characteristic extraction and dimension reduction of the original data. Concentrations of dissolved organic compound (DOC) and total nitrogen (TN), and turbidity were selected as the water quality prediction indices, a three-layer BPNN model was established for prediction, and the prediction performance was analyzed. The results showed that the optimal training-set sizes of concentrations of DOC and TN, and turbidity were 60, 60, and 90 days, while the best BPNN topological structures were 9-12-1, 8-6-1 and 7-13-1, respectively. The optimized BPNN model exhibited favorable prediction performance on the variation trends of concentrations of DOC and TN, and turbidity. In contrast, the prediction performance of DOC by BPNN model was significantly better than that of TN and turbidity, with RMSE, MAPE, and R values of 0.040, 0.66% and 0.867, respectively. This model had a good applicability and precision for prediction of surface water qualities having non-linear characteristics.
| [1] |
史斌, 姜继平, 王鹏.基于高频在线水质数据异常的突发污染预警[J]. 中国环境科学,2017,37(11):4394-4400. DOI: 10.3969/j.issn.1000-6923.2017.11.046
SHI B, JIANG J P, WANG P. Early warning of water pollution incidents based on abnormal change of water quality data from high frequency online monitoring[J]. China Environmental Science,2017,37(11):4394-4400. DOI: 10.3969/j.issn.1000-6923.2017.11.046
|
| [2] |
MAHAJAN A U, CHALAPATIRAO C V, GADKARI S K. Mathematical modeling: a tool for coastal water quality management[J]. Water Science and Technology,1999,40(2):151-157. DOI: 10.2166/wst.1999.0110
|
| [3] |
Fang X B, Zhang J Y, Chen Y X. QUAL2K model used in the water quality assessment of Qiantang River, China[J]. Water Environment Research,2008,80(11):2125-2133. DOI: 10.2175/106143008X304794
|
| [4] |
沈煜恒, 戴小鹏, 湛誉, 等.基于灰色模型的水质监测预警系统设计[J]. 农业工程,2019,9(8):31-34. DOI: 10.3969/j.issn.2095-1795.2019.08.011
SHEN Y H, DAI X P, ZHAN Y, et al. Design of water quality monitoring and early warning system based on grey model[J]. Agricultural Engineering,2019,9(8):31-34. DOI: 10.3969/j.issn.2095-1795.2019.08.011
|
| [5] |
李如忠.水质预测理论模式研究进展与趋势分析[J]. 合肥工业大学学报(自然科学版),2006,29(1):26-30.
|
| [6] |
黄俊, 周申范, 唐婉莹.TNT生化降解时间序列的人工神经网络预报模型[J]. 环境科学研究,2000,13(2):3-5. DOI: 10.3321/j.issn:1001-6929.2000.02.002
HUANG J, ZHOU S F, TANG W Y. Artificial neutral network predicting model of TNT biodegradation time series[J]. Research of Environmental Sciences,2000,13(2):3-5. DOI: 10.3321/j.issn:1001-6929.2000.02.002
|
| [7] |
李春华, 胡文, 叶春, 等.基于BP神经网络预测地表水净化装置总氮的去除效果[J]. 环境工程技术学报,2018,8(6):651-655. DOI: 10.3969/j.issn.1674-991X.2018.06.086
LI C H, HU W, YE C. Study on prediction of total nitrogen removal of a surface water purification device based on BP neural network[J]. Journal of Environmental Engineering Technology,2018,8(6):651-655. DOI: 10.3969/j.issn.1674-991X.2018.06.086
|
| [8] |
林佳敏, 陈金良, 林晶晶, 等.BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测[J]. 环境工程技术学报,2019,9(5):573-578. DOI: 10.12153/j.issn.1674-991X.2019.03.261
LIN J M, CHEN J L, LIN J J, et al. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology,2019,9(5):573-578. DOI: 10.12153/j.issn.1674-991X.2019.03.261
|
| [9] |
左朝晖, 李绍康, 杨津津, 等.基于GA-BP神经网络的页岩气开发区域水资源承载力研究[J]. 环境工程技术学报,2021,11(1):194-201. DOI: 10.12153/j.issn.1674-991X.20200081
ZUO Z H, LI S K, YANG J L, et al. Research on water resources carrying capacity of shale gas development area based on GA-BP neural network[J]. Journal of Environmental Engineering Technology,2021,11(1):194-201. DOI: 10.12153/j.issn.1674-991X.20200081
|
| [10] |
刘宇同. 一种基于人工神经网络的地表水质预测方法[D]. 哈尔滨: 哈尔滨工程大学, 2017.
|
| [11] |
赵文喜, 周滨, 刘红磊, 等.基于BP神经网络的海河干流叶绿素浓度短时预测研究[J]. 水利水电技术,2017,48(11):134-140.
|
| [12] |
张青, 王学雷, 张婷, 等.基于BP神经网络的洪湖水质指标预测研究[J]. 湿地科学,2016,14(2):212-218.
ZHANG Q, WANG X L, ZHANG T, et al. Prediction of water quality index of Honghu Lake based on back propagation neural network model[J]. Wetland Science,2016,14(2):212-218.
|
| [13] |
赵林, 戴天骄, 陈亮, 等.基于BP神经网络的桃林口水库水质预测[J]. 安徽农业科学,2019,47(24):73-77. DOI: 10.3969/j.issn.0517-6611.2019.24.024
ZHAO L, DAI T J, CHEN L, et al. Prediction of water quality in Taolinkou Reservoir based on BP neural network model[J]. Journal of Anhui Agricultural Sciences,2019,47(24):73-77. DOI: 10.3969/j.issn.0517-6611.2019.24.024
|
| [14] |
刘双印. 基于计算智能的水产养殖水质预测预警方法研究[D]. 北京: 中国农业大学, 2014.
|
| [15] |
袁红春, 赵彦涛, 刘金生.基于PCA-NARX神经网络的氨氮预测[J]. 大连海洋大学学报,2018,33(6):808-813.
YUAN H C, ZHAO Y T, LIU J S. Ammonia nitrogen level forecasting based on PCA-NARX neural network[J]. Journal of Dalian Ocean University,2018,33(6):808-813.
|
| [16] |
袁红春, 黄俊豪, 赵彦涛.基于PCA-NARX神经网络的溶解氧预测[J]. 山东农业大学学报(自然科学版),2019,50(5):902-907.
|
| [17] |
MULIA I E, TAY H, ROOPSEKHAR K, et al. Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations[J]. Journal of Hydro-Environment Research,2013,7(4):279-299. DOI: 10.1016/j.jher.2013.04.003
|
| [18] |
王佳楠. 基于BP神经网络的高锰酸盐指数预测研究 : 以渭南潼关吊桥段为例[D]. 西安: 长安大学, 2017.
|
| [19] |
肖金球, 周翔, 潘杨, 等.GA-BP优化TS模糊神经网络水质监测与评价系统预测模型的应用: 以太湖为例[J]. 西南大学学报(自然科学版),2019,41(12):110-119.
|
| [20] |
LACHENBRUCH P A, COHEN J. Statistical power analysis for the behavioral sciences[J]. Journal of the American Statistical Association,1989,84(408):1096.
|
| [21] |
查木哈, 卢志宏, 翟继武, 等.双隐含层BP神经网络模型在老哈河水质预测中的应用[J]. 水资源与水工程学报,2018,29(2):56-61. ⊕ DOI: 10.11705/j.issn.1672-643X.2018.02.10
|
| 1. |
陈静，李海洋. 基于GRA-GRU的淮河流域水质预测研究. 安全与环境学报. 2024(01): 376-387 .
| |
| 2. |
王涛，吴福雨，程紫微，王世杰，岳佳妮，樊小东，白淑叶，卢玺，肖萍，肖峰. 基于5参数GA-BP模型的出水水质预测——以宁夏某水厂为例. 环境保护科学. 2024(01): 163-170 .
| |
| 3. |
项新建，张颖超，许宏辉，厉阳，王世乾，郑永平. 基于CEEMDAN-VMD-TCN-lightGBM模型的水质预测研究. 中国农村水利水电. 2024(03): 86-95 .
| |
| 4. |
符敦凯，张云辉，徐小军，王鹰，许钟元，王杨双. 基于文献计量学的水质预测研究进展及趋势. 华东地质. 2024(01): 88-100 .
| |
| 5. |
冯子强，李登华，丁勇. 基于Blending-Clustering集成学习的大坝变形预测模型. 水利水电技术(中英文). 2024(04): 59-70 .
| |
| 6. |
徐东泽，肖琴. 基于小波与注意力机制的金融序列BiLSTM预测. 计算机仿真. 2024(05): 527-531 .
| |
| 7. |
秦艳，徐庆，陈晓倩，刘振鸿，唐亦舜，高品. 基于在线监测时间序列数据的水质预测模型研究进展. 东华大学学报(自然科学版). 2024(03): 116-122 .
| |
| 8. |
管业鹏，苏光耀，盛怡. 双向长短期记忆网络的时间序列预测方法. 西安电子科技大学学报. 2024(03): 103-112 .
| |
| 9. |
唐毅，徐全，杜彬，王磊，袁瑞豪，袁禹. 基于SARIMA-VMD-LSSVM的水产养殖溶解氧质量浓度预测. 江苏农业学报. 2024(08): 1473-1482 .
| |
| 10. |
李贤琪 ，廖孟光 ，林东方 . 优化BP神经网络模型的边坡安全多因素滚动预报. 工程勘察. 2024(11): 46-53+80 .
| |
| 11. |
习文双，江敏，吴昊，潘璠，唐燕. 基于PCA-LSTM神经网络的凡纳滨对虾养殖水质预测. 上海海洋大学学报. 2023(01): 108-117 .
| |
| 12. |
宋锐彪，刘海鸿. 基于小波分析的滑动GA-BP-GRACH模型对股票的预测研究. 数学的实践与认识. 2023(03): 117-126 .
| |
| 13. |
张凯. 基于EEMD-VotingRegressor的国控地表水体水质预测模型研究. 水利规划与设计. 2023(06): 79-83+89 .
| |
| 14. |
聂心童，孙超，何亚其，勾字文，马骏，田文尧. 超滤膜技术在废水处理领域中的应用研究进展. 当代化工研究. 2023(12): 5-7 .
| |
| 15. |
杨婧晖，梁涛. 石油工业废水处理技术研究现状及进展. 当代化工研究. 2023(17): 26-28 .
| |
| 16. |
张玉泽，姚立忠，罗海军. 基于量子加权最小门限单元网络的出水COD预测. 环境工程技术学报. 2023(05): 1857-1864 .
本站查看
| |
| 17. |
李晓梅，杨健浩，李俐，盖荣丽，汪祖民. 基于多维数据的WTD-GA-BP海参养殖水温预测. 河南师范大学学报(自然科学版). 2023(06): 30-38 .
| |
| 18. |
杨俊祺，范晓军，赵跃华，袁进. 基于PSO-BP神经网络的山西省碳排放预测. 环境工程技术学报. 2023(06): 2016-2024 .
本站查看
| |
| 19. |
黄珍，刘涛. 基于神经网络算法的精准施肥模型建立及仿真分析. 现代农业研究. 2023(11): 45-50 .
| |
| 20. |
李晓瑛，王华，曾一川，李保，袁伟皓，吴怡. 长江口国控断面水质参数演变的模型优化预测. 环境科学与技术. 2022(02): 165-173 .
|
Copyright © Editorial Department of Journal of Environmental Engineering Technology
Supported by: Beijing Renhe Information Technology Co., Ltd.
DownLoad: