Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Water Quality Samples
2.2. Fuzzy Rough Set Attribute Reduction
2.3. Entropy Method
2.4. Attribute Recognition Theoretical Model
3. Results and Discussion
3.1. Statistical Analysis
Parameters | I | II | III | IV | V |
---|---|---|---|---|---|
pH | 6–9 | ||||
DO (mg/L) | ≥7.5 | ≥6 | ≥5 | ≥3 | ≥2 |
CODMn (mg/L) | ≤2 | ≤4 | ≤6 | ≤10 | ≤15 |
COD (mg/L) | ≤15 | ≤15 | ≤20 | ≤30 | ≤40 |
BOD5 (mg/L) | ≤3 | ≤3 | ≤4 | ≤6 | ≤10 |
NH3-N (mg/L) | ≤0.15 | ≤0.5 | ≤1.0 | ≤1.5 | ≤2.0 |
TP (mg/L) | ≤0.02 | ≤0.1 | ≤0.2 | ≤0.3 | ≤0.4 |
TN (mg/L) | ≤0.2 | ≤0.5 | ≤1.0 | ≤1.5 | ≤2.0 |
F (mg/L) | ≤1.0 | ≤1.0 | ≤1.0 | ≤1.5 | ≤1.5 |
F. coli (cfu/L) | ≤200 | ≤2,000 | ≤10,000 | ≤20,000 | ≤40,000 |
Parameters | Min–Max | Median | Mean | SD | CV | Permissible Limits | MNEPL a |
---|---|---|---|---|---|---|---|
pH (a1) | 7.16–8.55 | 7.52 | 7.61 | 0.401 | 0.0527 | 6–9 | 0 |
DO (a2) | 4.8–13 | 7.7 | 8.44 | 2.6073 | 0.3089 | ≥5 | 1 |
CODMn (a3) | 3.12–6.48 | 5.04 | 5.209 | 0.9733 | 0.1868 | ≤6 | 2 |
COD (a4) | 12–23 | 16.5 | 16.8 | 3.49 | 0.2077 | ≤20 | 1 |
BOD5 (a5) | 1–4.6 | 2.4 | 2.69 | 1.4255 | 0.5299 | ≤4 | 3 |
NH3-N (a6) | 0.12–1.07 | 0.44 | 0.535 | 0.3868 | 0.7229 | ≤1.0 | 2 |
TP (a7) | 0.04–0.69 | 0.07 | 0.144 | 0.1978 | 1.3738 | ≤0.2 | 1 |
TN (a8) | 1.1–2.58 | 1.55 | 1.607 | 0.4423 | 0.2752 | ≤1.0 | 10 |
F (a9) | 0.24–0.38 | 0.3 | 0.298 | 0.0419 | 0.1404 | ≤1.0 | 0 |
F. coli (a10) | 20–24,196 | 1,514 | 3,793.4 | 7,227.91 | 1.9054 | ≤10,000 | 1 |
3.2. Parameters Attribute Reduction
Subset of Reserved Attributes | Subset of Deleted Attributes | β-Approximate Classification Quality | Delete a |
---|---|---|---|
{a2,a3,a4,a5,a6,a7,a8,a9,a10} | {a1} | 1 | Y |
{a3,a4,a5,a6,a7,a8,a9,a10} | {a1,a2} | 1 | Y |
{a4,a5,a6,a7,a8,a9,a10} | {a1,a2,a3} | 1 | Y |
{a5,a6,a7,a8,a9,a10} | {a1,a2,a3,a4} | 1 | Y |
{a6,a7,a8,a9,a10} | {a1,a2,a3,a4,a5} | 0.7 | N |
{a5,a7,a8,a9,a10} | {a1,a2,a3,a4,a6} | 0.2 | N |
{a5,a6,a8,a9,a10} | {a1,a2,a3,a4,a7} | 0.9 | N |
{a5,a6,a7,a9,a10} | {a1,a2,a3,a4,a8} | 1 | Y |
{a5,a6,a7,a10} | {a1,a2,a3,a4,a8,a9} | 1 | Y |
{a5,a6,a7} | {a1,a2,a3,a4,a8,a9,a10} | 0.6 | N |
3.3. Weights of Parameters
Parameters | Information Entropy | Weight |
---|---|---|
BOD5 | 0.8617 | 0.3701 |
NH3-N | 0.8579 | 0.3802 |
TP | 0.9528 | 0.1263 |
F. coli | 0.9539 | 0.1234 |
3.4. Water Quality Assessment
Methods | Reducts | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. |
---|---|---|---|---|---|---|---|---|---|---|---|
With attribute reduction | Reduct A | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅱ | Ⅱ | Ⅳ | Ⅱ |
Reduct B | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅱ | Ⅱ | Ⅳ | Ⅱ | |
Reduct C | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅱ | Ⅲ | Ⅲ | Ⅳ | Ⅱ | |
Reduct D | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | |
Reduct E | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅱ | Ⅱ | Ⅲ | Ⅱ | |
Reduct F | Ⅲ | Ⅱ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅱ | Ⅱ | Ⅳ | Ⅱ | |
Without attribute reduction | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅲ | Ⅱ | Ⅲ | Ⅲ | Ⅲ | Ⅱ |
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Li, Z.; Huang, G.; Zhang, Y.M.; Li, Y.P. Inexact two-stage stochastic credibility constrained programming for water quality management. Resour. Conserv. Recycl. 2013, 73, 122–132. [Google Scholar]
- Huang, Y.L.; Huang, G.H.; Liu, D.F.; Zhu, H.; Sun, W. Simulation-based inexact chance-constrained nonlinear programming for eutrophication management in the Xiangxi Bay of Three Gorges Reservoir. J. Environ. Manage. 2012, 108, 54–65. [Google Scholar] [CrossRef]
- Wang, F.; Wang, X.; Znao, Y.; Yang, Z.F. Long-term water quality variations and chlorophyll a simulation with an emphasis on different hydrological periods in Lake Baiyangdian, northern China. J. Environ. Inform. 2012, 20, 90–102. [Google Scholar] [CrossRef]
- Deviney, F.A., Jr.; Brown, D.E.; Rice, K.C. Evaluation of bayesian estimation of a hidden continuous-time markov chain model with application to threshold violation in water-quality indicators. J. Environ. Inform. 2012, 19, 70–78. [Google Scholar]
- Liu, D.J.; Zou, Z.H. Water quality evaluation based on improved fuzzy matter-element method. J. Environ. Sci. 2012, 24, 1210–1216. [Google Scholar] [CrossRef]
- Wang, X.J.; Zou, Z.H.; Zou, H. Using discriminant analysis to assess Polycyclic aromatic hydrocarbons contamination in Yongding New River. Environ. Monit. Assess. 2013, 185, 8547–8555. [Google Scholar] [CrossRef]
- Shrestha, S.; Kazama, F.; Nakamura, T. Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong River. J. Hydroinform. 2008, 10, 43–56. [Google Scholar] [CrossRef]
- Ni, S.H.; Bai, Y.H. Application of BP neural network model in groundwater quality evaluation. Syst. Eng.-Theory Pract. 2000, 20, 124–127. [Google Scholar]
- Hou, D.B.; He, H.M.; Huang, P.J.; Zhang, G.X.; Loaiciga, H. Detection of water-quality contamination events based on multi-sensor fusion using an extended Dempster-Shafer method. Meas. Sci. Technol. 2013, 24. [Google Scholar] [CrossRef]
- Sun, J.N.; Zou, Z.H.; Ren, G.P. Study on the fuzzy synthetic evaluation for natural water quality. Technol. Equip. Environ. Pollut. Control 2005, 6, 45–48. [Google Scholar]
- Zou, Z.H.; Sun, J.N.; Ren, G.P. Study and application on the entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Acta Sci. Circumstantiae 2005, 25, 552–556. [Google Scholar]
- Li, P.Y.; Qian, H.; Wu, J.H. Groundwater quality assessment based on improved water quality index in Pengyang County, Ningxia, Northwest China. E-J. Chem. 2010, 7, S209–S216. [Google Scholar] [CrossRef]
- Li, P.Y.; Qian, H.; Wu, J.H. Hydrochemical formation mechanisms and quality assessment of groundwater with improved TOPSIS method in Pengyang County Northwest China. E-J. Chem. 2011, 8, 1164–1173. [Google Scholar]
- Li, P.Y.; Wu, J.H.; Qian, H. Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China. Environ. Monit. Assess. 2012, 184, 4841–4854. [Google Scholar] [CrossRef]
- Cheng, Q.S. Attribute recognition theoretical model with application. Acta Sci. Nat. Univ. Pekin. 1997, 33, 12–20. [Google Scholar]
- Li, P.Y.; Wu, J.H.; Qian, H. Groundwater quality assessment based on entropy weighted osculating value method. Int. J. Environ. Sci. 2010, 1, 621–630. [Google Scholar]
- Li, Z.W.; Fang, Y.; Zeng, G.M.; Li, J.B.; Zhang, Q.; Yuan, Q.S.; Wang, Y.M.; Ye, F.Y. Temporal and spatial characteristics of surface water quality by an improved universal pollution index in red soil hilly region of South China: A case study in Liuyanghe River watershed. Environ. Geol. 2009, 58, 101–107. [Google Scholar] [CrossRef]
- Li, P.Y.; Qian, H.; Wu, J.H. Application of set pair analysis method based on entropy weight in groundwater quality assessment—A case study in Dongsheng City, Northwest China. E-J. Chem. 2011, 8, 851–858. [Google Scholar] [CrossRef]
- Gamble, A.; Babbar-Sebens, M. On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin, Indiana, USA. Environ. Monit. Assess. 2012, 184, 845–875. [Google Scholar] [CrossRef]
- Zhang, X.; Wang, Q.; Liu, Y.F.; Wu, J.; Yu, M. Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong. Environ. Monit. Assess. 2011, 173, 17–27. [Google Scholar] [CrossRef]
- Pawlak, Z. Rough set. Int. J. Comput. Inform. Sci. 1982, 11, 341–356. [Google Scholar] [CrossRef]
- Ziarko, W. Variable precision rough set model. J. Comput. Syst. Sci. 1993, 46, 39–59. [Google Scholar] [CrossRef]
- Pawlak, Z.; Skowron, A. Rudiments of rough sets. Inform. Sci. 2007, 177, 3–27. [Google Scholar] [CrossRef]
- Yanto, I.T.R.; Vitasari, P.; Herawan, T.; Deris, M.M. Applying variable precision rough set model for clustering student suffering study’s anxiety. Expert Syst. Appl. 2012, 39, 452–459. [Google Scholar] [CrossRef]
- He, Q.; Wu, C.X.; Chen, D.G.; Zhao, S.Y. Fuzzy rough set based attribute reduction for information systems with fuzzy decisions. Knowl.-Based Syst. 2011, 24, 689–696. [Google Scholar] [CrossRef]
- Guo, M.; Zhu, J.F. The performance evaluation in logistics service supply chain based on fuzzy-rough sets. Syst. Eng. 2007, 25, 48–52. [Google Scholar]
- Zhang, K.; Chi, G.T. Establishment of ecological evaluation indicators system based on correlation analysis-rough set theory. J. Syst. Eng. 2012, 27, 119–128. [Google Scholar]
- Zhou, S.M.; Wen, L.; Ye, Z.X.; Xu, W. Study on nuclear accident emergency decision based on attribute reduction algorithm. Radiat. Prot. 2011, 31, 100–104. [Google Scholar]
- Li, W.W. Water quality evaluation model for Three Gorges Reservoir area based on rough set and roughness element neural network. Comput. Appl. Softw. 2011, 28, 193–196. [Google Scholar]
- Wang, G.S. Study on water quality assessment of Songhuajiang River based on PSO-PPE model. Water Conserv. Sci. Technol. Econ. 2013, 19, 27–29. [Google Scholar]
- Chen, S.Z.; Wang, X.J.; Zhao, X.J. An attribute recognition model based on entropy weight for evaluating the quality of groundwater sources. J. China Univ. Min. Technol. 2008, 18, 72–75. [Google Scholar] [CrossRef]
- Wang, L.J.; Zou, Z.H. Application of improved attributes recognition method in water quality assessment. Chin. J. Environ. Eng. 2008, 2, 553–556. [Google Scholar]
- Men, B.H.; Liang, C. Attribute recognition model-based variation coefficient weight for evaluating water quality. J. Harbin Inst. Technol. 2005, 37, 1373–1375. [Google Scholar]
- Zhang, X.Q.; Liang, C.; Liu, H.Q. Application of attribute recognition model based on coefficient of entropy to comprehensive evaluation of groundwater quality. J. Sichuan Univ.: Eng. Sci. Ed. 2005, 37, 28–31. [Google Scholar]
- Lessels, J.S.; Bishop, T.F.A. Estimating water quality using linear mixed models with stream discharge and turbidity. J. Hydrol. 2013, 498, 13–22. [Google Scholar] [CrossRef]
© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Share and Cite
An, Y.; Zou, Z.; Li, R. Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model. Int. J. Environ. Res. Public Health 2014, 11, 3507-3520. https://doi.org/10.3390/ijerph110403507
An Y, Zou Z, Li R. Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model. International Journal of Environmental Research and Public Health. 2014; 11(4):3507-3520. https://doi.org/10.3390/ijerph110403507
Chicago/Turabian StyleAn, Yan, Zhihong Zou, and Ranran Li. 2014. "Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model" International Journal of Environmental Research and Public Health 11, no. 4: 3507-3520. https://doi.org/10.3390/ijerph110403507