Determination of the principal factors of river water quality through cluster analysis method and its prediction

doi:10.1007/s11783-011-0382-7

Front Envir Sci Eng

2012, Vol. 6

Issue (2) : 238-245 https://doi.org/10.1007/s11783-011-0382-7

RESEARCH ARTICLE

Determination of the principal factors of river water quality through cluster analysis method and its prediction

Liang GUO, Ying ZHAO, Peng WANG(

)

State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China

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Abstract

In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (COD_Mn) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the COD_Mn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.

Keywords water quality forecast principal factor cluster analysis method artificial neural network

Corresponding Author(s): WANG Peng,Email:pwang73@hit.edu.cn

Issue Date: 01 April 2012

Cite this article:

Liang GUO,Ying ZHAO,Peng WANG. Determination of the principal factors of river water quality through cluster analysis method and its prediction[J]. Front Envir Sci Eng, 2012, 6(2): 238-245.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-011-0382-7
https://academic.hep.com.cn/fese/EN/Y2012/V6/I2/238

time	value	temperature/°C	turbidity/(NTU)	chromaticity/(NTU)	quantity of water/(m³·h^-1)	pH	alkalinity/(μS·cm^-1)	NH₃/(mg·L^-1)	NO3-/(mg·L-1)	conductivity/(μS·cm^-1)	the next day’s COD_Mn/(mg·L^-1)
January	range	0–1.5	5.42–27.2	20–74	7670–10500	6.9–7.2	52–131	1.1–4.4	0.0028–0.036	109–209	4.32–6.91
	mean	0.14	16.19	34.16	9158.33	7.03	84.86	2.20	0.011	159.60	5.54
	S. D.	0.3656	7.7881	11.6067	590.0823	0.0897	19.9706	0.9041	0.0084	27.9514	0.6239
February	range	0–1	4.37–17.7	20–36	7700–10170	6.88–7	72–104	1.7–4.4	0.004–0.034	148–203	4.74–6.52
	mean	0.054	8.52	27.52	8895.54	6.92	87.64	3.10	0.01	173.25	5.59
	S. D.	0.2272	3.1403	4.6043	562.0641	0.0307	5.2307	0.9632	0.0097	13.3420	0.3537
March	range	0–3	2.89–17.3	18–40	7600–10960	6.88–7.35	60–92	1.2–3.6	0.005–0.136	125–183	4.12–6.74
	mean	0.89	7.39	27.03	9543.80	7.02	74.78	2.27	0.019	147.72	5.26
	S. D.	0.9660	2.6050	5.5696	721.7863	0.1004	6.6147	0.5922	0.0238	14.8363	0.6176
April	range	0–15	5.59–221	20–250	4200–11210	6.9–7.73	56–122	0.6–2	0.005–0.06	114–229	4.16–11.55
	mean	6.51	55.58	65.38	9366.16	7.29	71.70	1.37	0.017	152.21	6.73
	S. D.	5.1411	53.8544	41.6770	1029.2740	0.1986	11.1166	0.3306	0.01259	27.7722	1.5990
May	range	10–32	17.7–277	45–200	7670–10290	6.95–8.41	46–122	0.16–3	0.007–0.06	140–305	4.91–12.48
	mean	14.76	60.40	85.65	9266.26	7.55	75	0.60	0.02	189.88	8.16
	S. D.	8.8337	51.0282	36.8634	594.0308	0.3424	17.9358	0.3992	0.0113	46.7278	1.2248
June	range	15–28	15.8–322	50–250	7830–10800	6.93–8.28	52–120	0.2–2.4	0.007–0.08	135–385	5.38–14.02
	mean	20.95	75.71	114.28	99580.23	7.63	82.02	0.57	0.03	225.11	8.24
	S. D.	2.8443	55.3232	52.2344	677.9957	0.2983	14.5886	0.4143	0.0170	48.9931	1.6845
July	range	21–28	40.3–349	60–500	7860–10870	6.81–7.88	44–100	0.12–3.2	0.005–0.1	94–462	5.12–15.83
	mean	25.54	108.80	157.47	9486.99	7.39	73.85	1.15	0.03	212.41	9.56
	S. D.	1.4108	79.6504	95.7568	832.4633	0.2933	16.7247	1.2432	0.0266	75.5895	2.5221
August	range	20–27	64.8–803	80–1100	6450–11730	6.91–7.71	50–102	0.14–3	0.003–0.05	106–249	6.19–24.64
	mean	23.09	221.86	285.27	9622.42	7.30	69.16	0.63	0.02	166.82	11.35
	S. D.	1.5029	174.1045	242.6627	1234.2080	0.2208	12.3273	0.6162	0.0104	43.0092	4.0928
September	range	13–24	53.4–197	40–350	7160–11620	7–7.77	49–138	0.04–3.6	0.002–0.044	97–242	4.43–17.06
	mean	17.77	95.21	121.03	10019.31	7.36	76.89	0.51	0.01	166.05	8.95
	S. D.	3.0792	29.6532	55.9440	892.7835	0.2088	24.5364	0.7029	0.0086	43.5647	3.2455
October	range	3–18	36.1–258	50–359	5300–11840	7–8.25	46–154	0.15–0.7	0.001–0.017	54–282	4.1–14.8
	mean	8.91	79.27	127.04	10067.93	7.39	84.89	0.40	0.01	156.87	7.48
	S. D.	3.4279	29.3118	77.0548	1124.0610	0.2268	28.4584	0.0707	0.0051	52.6008	1.9778
November	range	0–9	11.3–134	15–467	5500–11050	6.95–7.55	60–134	0.24–2	0.001–0.017	108–278	3.29–9.44
	mean	1.74	49.30	117.30	9388.64	7.27	91.99	0.8714	0.0096	155.93	5.51
	S. D.	1.5881	35.2144	82.1247	1167.359	0.1485	23.2341	0.4576	0.0036	31.7304	1.5251
December	range	0–1.5	6.18–44.2	15–114	6300–10570	6.88–7.38	62–144	0.52–4.44	0.005–0.027	128–234	3.52–7.13
	mean	0.25	14.42	49.84	9113.66	7.14	96.33	1.80	0.01	170.78	5.24
	S. D.	0.4461	7.5479	27.4536	755.4308	0.1270	23.1457	0.8903	0.0047	26.5694	0.7472
number		1	2	3	4	5	6	7	8	9	10

Tab.1 Water quality characteristics of Songhua River and the number of variables

Fig.1 Structure drawing of neural network forecasting model

Tab.2 Procedure of cluster analysis

Fig.2 Sequence of cluster analysis

Tab.3 Forecasting values and practical measured values of COD by traditional method/(mg·L)

Fig.3 COD comparison curves of forecasting values and observed values by traditional method

Fig.4 Forecasting error curves of COD values by traditional method

Tab.4 Forecasting values and observed values of COD by cluster analysis method/(mg·L)

Fig.5 COD comparison curves of forecasting values and observed values by cluster analysis method

Fig.6 Forecasting error curves of COD values by cluster amalysis method

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