| Citation: | CHEN W,KIM J S,YU J W,et al.Forecasting CODMn of Poyang Lake based on empirical wavelet transform[J].Journal of Environmental Engineering Technology，2023，13（1）：180-187 doi: 10.12153/j.issn.1674-991X.20210592
|
Permanganate index (CODMn) is one of the most important parameters to measure water quality and could reflect the degree of water pollution by reducing substances. A novel CODMn forecast model (EWT-BLSTM) by combining empirical wavelet transform (EWT) and bidirectional long short-term memory (BLSTM) neural network was proposed. First, the original CODMn time series was decomposed into several components by EWT. Next, BLSTM neural network was employed to predict each decomposed component. Finally, the predictions of all components were reconstructed to obtain the new hybrid model EWT-BLSTM for the final CODMn predictions. CODMn data of Poyang Lake was used to evaluate the proposed forecast model. The results showed that EWT-BLSTM model had a powerful forecast capacity. For 1, 7-day ahead forecasting, the mean absolute percentage error (MAPE) of the forecast by EWT-BLSTM was 2.25% and 8.36%, respectively. The MAPE reduced by EWT-BLSTM over BLSTM was 10.53% for 1-day ahead forecasting and 16.16% for 7-day ahead forecasting. Furthermore, the proposed model showed highly stable forecasting performance for CODMn peak points, indicating that the proposed method was still applicable in the case of relatively complex data with extreme points.
| [1] |
CARVALHO L, POIKANE S, LYCHE SOLHEIM A, et al. Strength and uncertainty of phytoplankton metrics for assessing eutrophication impacts in lakes[J]. Hydrobiologia,2013,704(1):127-140. doi: 10.1007/s10750-012-1344-1
|
| [2] |
WU Z S, ZHANG D W, CAI Y J, et al. Water quality assessment based on the water quality index method in Lake Poyang: the largest freshwater lake in China[J]. Scientific Reports,2017,7:17999. doi: 10.1038/s41598-017-18285-y
|
| [3] |
王圣瑞, 舒俭民, 倪兆奎, 等.鄱阳湖水污染现状调查及防治对策[J]. 环境工程技术学报,2013,3(4):342-349. doi: 10.3969/j.issn.1674-991X.2013.04.054
WANG S R, SHU J M, NI Z K, et al. Investigation on pollution situation and countermeasures in Poyang Lake[J]. Journal of Environmental Engineering Technology,2013,3(4):342-349. doi: 10.3969/j.issn.1674-991X.2013.04.054
|
| [4] |
ZHANG S S, WEI J, LI Y P, et al. The Influence of seasonal water level fluctuations on the soil nutrients in a typical wetland reserve in Poyang Lake, China[J]. Sustainability,2021,13(7):3846. doi: 10.3390/su13073846
|
| [5] |
PU J, WANG S R, NI Z K, et al. Implications of phosphorus partitioning at the suspended particle-water interface for lake eutrophication in China's largest freshwater lake, Poyang Lake[J]. Chemosphere,2021,263:128334. doi: 10.1016/j.chemosphere.2020.128334
|
| [6] |
许加星, 徐力刚, 姜加虎, 等.鄱阳湖典型洲滩植物群落结构变化及其与土壤养分的关系[J]. 湿地科学,2013,11(2):186-191. doi: 10.3969/j.issn.1672-5948.2013.02.006
XU J X, XU L G, JIANG J H, et al. Change of vegetation community structure and the relationship between it and soil nutrients in typical beaches in Poyang Lake area[J]. Wetland Science,2013,11(2):186-191. doi: 10.3969/j.issn.1672-5948.2013.02.006
|
| [7] |
王子为, 林佳宁, 张远, 等.鄱阳湖入湖河流氮磷水质控制限值研究[J]. 环境科学研究,2020,33(5):1163-1169. doi: 10.13198/j.issn.1001-6929.2020.03.45
WANG Z W, LIN J N, ZHANG Y, et al. Water quality limits of nitrogen and phosphorus in the inflow rivers of Poyang Lake[J]. Research of Environmental Sciences,2020,33(5):1163-1169. doi: 10.13198/j.issn.1001-6929.2020.03.45
|
| [8] |
谢培, 高峰, 王书航, 等.入湖河流对千岛湖水质影响研究: 以CODMn为例[J]. 环境工程技术学报,2019,9(6):692-700. doi: 10.12153/j.issn.1674-991X.2019.04.300
XIE P, GAO F, WANG S H, et al. Study on the influence of inflowing rivers on the water quality of Qiandao Lake: taking CODMn as an example[J]. Journal of Environmental Engineering Technology,2019,9(6):692-700. doi: 10.12153/j.issn.1674-991X.2019.04.300
|
| [9] |
王业耀, 姜明岑, 李茜, 等.流域水质预警体系研究与应用进展[J]. 环境科学研究,2019,32(7):1126-1133. doi: 10.13198/j.issn.1001-6929.2019.05.01
WANG Y Y, JIANG M C, LI Q, et al. Advances in watershed water quality early-warning system[J]. Research of Environmental Sciences,2019,32(7):1126-1133. doi: 10.13198/j.issn.1001-6929.2019.05.01
|
| [10] |
RUBEN G B, ZHANG K, BAO H J, et al. Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand[J]. Water Resources Management,2018,32(1):273-283. doi: 10.1007/s11269-017-1809-0
|
| [11] |
MIAO S, ZHOU C L, ALQAHTANI S A, et al. Applying machine learning in intelligent sewage treatment: a case study of chemical plant in sustainable cities[J]. Sustainable Cities and Society,2021,72:103009. doi: 10.1016/j.scs.2021.103009
|
| [12] |
Khullar S, Singh N. Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation[J/OL]. Environmental Science and Pollution Research International, 2021. DOI: 10.1007/s11356-021-13875-w.
|
| [13] |
EZE E, AJMAL T. Dissolved oxygen forecasting in aquaculture: a hybrid model approach[J]. Applied Sciences,2020,10(20):7079. doi: 10.3390/app10207079
|
| [14] |
ZOU Q H, XIONG Q Y, LI Q D, et al. A water quality prediction method based on the multi-time scale bidirectional long short-term memory network[J]. Environmental Science and Pollution Research International,2020,27(14):16853-16864. doi: 10.1007/s11356-020-08087-7
|
| [15] |
FIJANI E, BARZEGAR R, DEO R, et al. Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters[J]. Science of the Total Environment,2019,648:839-853. doi: 10.1016/j.scitotenv.2018.08.221
|
| [16] |
HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences,1998,454(1971):903-995. doi: 10.1098/rspa.1998.0193
|
| [17] |
LIU S Y, XU L Q, LI D L. Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks[J]. Computers & Electrical Engineering,2016,49:1-8.
|
| [18] |
LIANG N, ZOU Z H, WEI Y G. Regression models (SVR, EMD and FastICA) in forecasting water quality of the Haihe River of China[J]. Desalination and Water Treatment,2019,154:147-159. doi: 10.5004/dwt.2019.24034
|
| [19] |
WANG Y X, YUAN Y, PAN Y, et al. Modeling daily and monthly water quality indicators in a canal using a hybrid wavelet-based support vector regression structure[J]. Water,2020,12(5):1476. doi: 10.3390/w12051476
|
| [20] |
GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing,2013,61(16):3999-4010. doi: 10.1109/TSP.2013.2265222
|
| [21] |
LIU H, YANG R, DUAN Z, et al. A hybrid neural network model for marine dissolved oxygen concentrations time-series forecasting based on multi-factor analysis and a multi-model ensemble[J/OL]. Engineering, 2021. DOI: 10.1016/j.eng.2021.10.005.
|
| [22] |
NONG X Z, SHAO D G, SHANG Y M, et al. Analysis of spatio-temporal variation in phytoplankton and its relationship with water quality parameters in the South-to-North Water Diversion Project of China[J]. Environmental Monitoring and Assessment,2021,193(9):1-18.
|
| [23] |
SIT M, DEMIRAY B Z, XIANG Z, et al. A comprehensive review of deep learning applications in hydrology and water resources[J]. Water Science and Technology,2020,82(12):2635-2670. doi: 10.2166/wst.2020.369
|
| [24] |
CHEN Y Y, SONG L H, LIU Y Q, et al. A review of the artificial neural network models for water quality prediction[J]. Applied Sciences,2020,10(17):5776. doi: 10.3390/app10175776
|
| [25] |
YE Q Q, YANG X Q, CHEN C B, et al. River water quality parameters prediction method based on LSTM-RNN Model[J]. 2019 Chinese Control and Decision Conference (CCDC),2019:3024-3028.
|
| [26] |
LIU Y Q, ZHANG Q, SONG L H, et al. Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction[J]. Computers and Electronics in Agriculture,2019,165:104964. ◇ doi: 10.1016/j.compag.2019.104964
|
Figures(6) / Tables(6)
Copyright © Editorial Department of Journal of Environmental Engineering Technology
Supported by: Beijing Renhe Information Technology Co., Ltd.
DownLoad:
