In order to effectively monitor water quality, this paper proposes a data fusion method based on Dempster-Shafer evidence theory to detect pollutants in water. Our proposed water quality monitoring system is organized as a hierarchical structure, and the whole monitoring area is divided into several parts. The water quality monitoring system includes an online monitoring module and an offline monitoring module. In particular, each monitoring area has a cluster that contains several wireless sensor nodes to collect data and communicate with other sensor nodes. Furthermore, multiple water quality parameters are detected in our water quality monitoring system, such as PH, conductivity, temperature, dissolved oxygen, turbidity, etc. The final water quality monitoring decisions are made by fusing various types of water quality indexes using the Dempster-Shafer evidence theory. Finally, experimental results prove that the proposed method can detect pollutants in water with higher accuracy by effectively fusing various types of water quality indexes.
water quality monitoring, wireless sensor network, data fusion, dempster-shafer evidence theory, ROC curve.
1. W.W Yan, J.L Li, X.H Bai, Comprehensive assessment and visualized monitoring of urban drinking water quality, 2016, Chemometrics and intelligent laboratory systems, no. 155, pp 26-35.
2. G. Durrieu, Q.K Pham, A.S Foltete, V. Maxime, I. Grama, L.T Veronique, H. Duval, J.M Tricot, C.B Naceur, O. Sire, Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry, 2016, Environmental monitoring and assessment, vol. 188, no. 7, pp 1-8.
3. K.E McCracken., S.V Angus., K.A Reynolds, J.Y Yoon, Multimodal Imaging and lighting bias correction for improved mu PAD-based water quality monitoring via smartphones, 2016, Scientific reports, no. 6, pp 27529.
4. M. Kim, Y. Kim, H. Kim, W. Piao, C. Kim, Enhanced monitoring of water quality variation in Nakdong River downstream using multivariate statistical techniques, 2016, Desalination and water treatment, vol. 57, no. 27, pp 12508-12517.
5. F. Ge, Y.N Wang, Energy efficient networks for monitoring water quality in subterranean rivers, 2016, Sustainability, vol. 8, no. 6, pp 526.
6. D.C Barrett, A.E Frazier, Automated method for monitoring water quality using landsat imagery, 2016, Water, vol. 8, no. 6, pp 257.
7. I.B. Roll, R.U Halden, Critical review of factors governing data quality of integrative samplers employed in environmental water monitoring, 2016, Water research, no. 94, pp 200-207.
8. J.F. Griffith, S.B. Weisberg, B.F Arnold, Y.P Cao, K.C. Schiff, J.M.J Colford., Epidemiologic evaluation of multiple alternate microbial water quality monitoring indicators at three California beaches, 2016, Water research, no. 94, pp 371-381.
9. M.Q Wu, C.Y Wu, W.J Huang, Z. Niu, C.Y Wang, W. Li, P.Y Hao, An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery, 2016, Information fusion, no. 31, pp 14-25.
10. K.M. Nunes, M.V Andrade, A.M Santos Filho, M.C. Lasmar, M.M Sena, Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy, 2016, Food chemistry, no. 205, pp 14-22.
11. S. Kumar, R.M., Hegde, Multi-sensor data fusion methods for indoor localization under collinear ambiguity, 2016, Pervasive and mobile computing, no. 30, pp 18-31.
12. E. Borras, J. Ferre, R. Bogue, M. Mestres, L. Acena, A. Calvo, O. Busto, Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression, 2016, Talanta, no. 155, pp 116-123.
13. E. Borras, J. Ferre, R. Boque, M. Mestres, L. Acena, A. Calvo, O. Busto, Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA), 2016, Food chemistry, no. 203, pp 314-322.
14. A. Schoech, A. Salvadori, S. Carmignato, E. Savio, Enhancing multisensor data fusion on light sectioning coordinate measuring systems for the in-process inspection of freeform shaped parts, 2016, Precision engineering-journal of the international societies for precision engineering and nanotechnology, no. 45, pp 209-215.
15. D. Yang, H. Li, Y.G Hu, J. Zhao, H.W Xiao, Y.S Lan, Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion, 2016, Renewable energy, no. 92, pp 104-116.
16. A. Schlicker, M. Michaut, R. Rahman, L.F.A. Wessels, OncoScape: Exploring the cancer aberration landscape by genomic data fusion, 2016, Scientific reports, no. 6, pp 28103.
17. B. Gomathi, P. Sakthivel, Enhancing information retrieval process using data fusion by ABC weighted based fuzzy retrieval in Health Care Analytic Software, 2016, Journal of medical imaging and health informatics, vol.6, no. 3, pp 863-868.
18. F.H Bijarbooneh, W. Du, E.C.H. Ngai, X.M Fu, J.C Liu, Cloud-assisted data fusion and sensor selection for internet of things, 2016, IEEE Internet of things journal, vol. 3, no. 3, pp 257-268.
19. Y.H Zhang, S. Prasad, Multisource geospatial data fusion via Local Joint sparse representation, 2016, IEEE Transactions on geoscience and remote sensing, vol. 54, no. 6, pp 3265-3276.
20. Q.F Zhou, H. Zhou, Q.Q Zhou, F. Yang, L.K Luo, T. Li, Structural damage detection based on posteriori probability support vector machine and dempster-shafer evidence theory, 2015, Applied soft computing, no. 36, pp 368-374.
21. Z.W Li, G.Q Wen, N.X Xie, An approach to fuzzy soft sets in decision making based on grey relational analysis and dempster-shafer theory of evidence: an application in medical diagnosis, 2015, Artificial intelligence in medicine, vol. 64, no. 3, pp 161-171.
22. M. Compare, E. Zio, Genetic algorithms in the framework of dempster-shafer theory of evidence for maintenance optimization problems, 2015, IEEE Transactions on reliability, vol. 64, no. 2, pp 645-660.
23. H.X Tang, A novel fuzzy soft set approach in decision making based on grey relational analysis and dempster-shafer theory of evidence, 2015, Applied soft computing, no. 31, pp 317-325.
24. K. Benmouiza, M. Tadj, A. Cheknane, Classification of hourly solar radiation using fuzzy c-means algorithm for optimal stand-alone PV system sizing, 2016, International journal of electrical power & energy systems, no. 82, pp 233-241.
25. O. Kesemen, O. Tezel, E. Ozkul, Fuzzy c-means clustering algorithm for directional data (FCM4DD), 2016, Expert systems with applications, no. 58, pp 76-82.
26. E. Esme, B. Karlik, Fuzzy c-means based support vector machines classifier for perfume recognition, 2016, Applied soft computing, no. 46, pp 452-458.