基于季节性分解与长短期记忆网络的水质动态预警

doi:10.11959/j.issn.2096-6652.202145

Abstract

Abstract:

Surface water quality is increasingly deteriorated in recent years, and therefore, high-quality early warning and prediction of water quality are essential for sustainability of water resources and emergency response mechanisms.Long short-term memory (LSTM) network is widely applied in the existing literature on the prediction of water quality time series.However, only applying LSTM for the prediction of water quality time series cannot well address irregular fluctuations in the water quality series caused by multiple complex factors.To solve this problem, a data-driven prediction model for the water quality time series was proposed, named STL-LSTM-ED, which was composed of seasonal-trend decomposition using locally weighted scatterplot smoothing (STL) and LSTM based on encoder-decoder (LSTM-ED).Compared with several typical models of LSTM, LSTM-ED, and a sequence decomposition method based on LSTM, the proposed STL-LSTM-ED can significantly improve the prediction accuracy and reliability of the water quality time series, and also provide the effective data support for dynamic warning of water quality.

Key words: seasonal decomposition, long short-term memory network, dynamicwarning of water quality, anomaly detection

CLC Number:

TP399

Bowen XU, Jing BI, Haitao YUAN, et al. Dynamic water quality warning with seasonal decomposition and long short-term memory network[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(4): 456-465.

Figures/Tables 15

步长	LSTM			LSTM-ED
步长	MAPE	MSE	RMSE	MAPE	MSE	RMSE
1	10.71	0.91	1.15	6.92	0.56	0.79
2	10.96	0.94	1.18	7.07	0.57	0.8
3	11.34	0.97	1.22	7.21	0.59	0.81
4	11.77	1.01	1.27	7.28	0.62	0.83
5	12.2	1.05	1.32	7.35	0.64	0.83
6	12.63	1.09	1.37	7.55	0.69	0.86
7	13.02	1.13	1.42	7.62	0.71	0.87
8	13.37	1.16	1.46	7.69	0.73	0.89
9	13.69	1.19	1.5	8.17	0.76	0.92
10	13.97	1.22	1.53	8.49	0.78	0.93
11	14.2	1.24	1.56	8.91	0.83	0.95
12	14.41	1.26	1.59	9.23	0.87	0.99
13	14.58	1.28	1.61	9.49	0.9	1.02
14	14.73	1.29	1.63	9.88	0.92	1.05
15	14.85	1.3	1.65	10.32	0.94	1.08
16	14.95	1.31	1.66	10.77	0.97	1.1
17	15.04	1.32	1.67	11.51	0.99	1.13
18	15.1	1.33	1.68	11.89	1.04	1.16
19	15.15	1.33	1.69	12.21	1.13	1.19
20	15.19	1.34	1.69	12.65	1.19	1.21
平均	13.59	1.18	1.49	$9 . 11$	$0 . 82$	$0 . 97$

步长	EMD-LSTM-ED			STL-LSTM-ED
步长	MAPE	MSE	RMSE	MAPE	MSE	RMSE
1	3.21	0.28	0.46	3.73	0.33	0.43
2	3.4	0.3	0.47	3.97	0.35	0.45
3	3.69	0.32	0.5	4.22	0.37	0.48
4	4.05	0.35	0.53	4.48	0.39	0.51
5	4.44	0.39	0.57	4.75	0.41	0.54
6	4.86	0.43	0.62	5.01	0.44	0.57
7	5.29	0.47	0.66	5.27	0.46	0.6
8	5.73	0.5	0.71	5.53	0.48	0.63
9	6.16	0.54	0.76	5.79	0.5	0.65
10	6.59	0.58	0.8	6.04	0.52	0.68
11	7.02	0.62	0.85	6.28	0.55	0.71
12	7.43	0.66	0.9	6.52	0.57	0.74
13	7.83	0.7	0.94	6.74	0.59	0.76
14	8.21	0.73	0.99	6.96	0.6	0.79
15	8.58	0.76	1.03	7.16	0.62	0.81
16	8.93	0.8	1.07	7.36	0.64	0.84
17	9.26	0.83	1.11	3.73	0.33	0.43
18	9.57	0.86	1.15	3.97	0.35	0.45
19	9.86	0.88	1.18	4.22	0.37	0.48
20	10.13	0.91	1.22	4.48	0.39	0.51
平均	6.71	0.60	0.83	$5 . 31$	$0 . 46$	$0 . 60$

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真实情况	预测为正例	预测为反例
正例	TP	FN
反例	FP	TN

k值	F1-Score	AUC
3	0.494	0.62
4	0.516	0.67
5	0.501	0.64
6	0.472	0.60
7	0.463	0.57
8	0.455	0.56

Dynamic water quality warning with seasonal decomposition and long short-term memory network

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