Modeling and Prediction of Surface Water Contamination using On-line Sensor Data


Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 7 , ISSUE 5 (December 2014) > List of articles

Special issue ICST 2014

Modeling and Prediction of Surface Water Contamination using On-line Sensor Data

Tochukwu K. Anyachebelu / Marc Conrad / Tahmina Ajmal

Keywords : Water quality, sensors, prediction, statistics

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 5, Pages 1-5, DOI:

License : (CC BY-NC-ND 4.0)

Published Online: 15-February-2020



Water contamination is a great disadvantage to humans and aquatic life. Maintaining the aesthetics and quality of water bodies is a priority for environmental stake holders. The water quality sensor data can be analyzed over a period of time to give an indication of pollution incidents and could be a useful forecasting tool. Here we show our initial finding from statistical analysis on such sensor data from one of the lakes of the river Lea, south of Luton. Our initial work shows patterns which will form the basis for our forecasting model.

Content not available PDF Share



[1] E. O’Connor, A. F. Smeaton, N. E. O’Connor and F. Regan, "A neural network approach to smarter sensor networks for water quality monitoring," Sensors, vol. 12, pp. 4605-4632, 2012. 

[2] Parliament. UK Technical Advisory Group on the Water Framework Directive. (2013) Updated Recommendations on Environmental Standards River Basin Management ( 2015 - 21 ). UNITED KINGDOM: WFD UK TAG.

[3] B. Alejandra, L. Shuming and V. Francois, "Drinking Water Source Contamination Early Warning System and Modelling in China: A Review," International Journal of Environmental Pollution and Remediation, vol. 1, pp. 13-19, 2012.

[4] Jin-Suo Lu, Ting-Lin Huang and Chun-yan Wang, "Data mining on source water quality (tianjin, china) for forecasting algae bloom based on artificial neural network (ANN)," in Computer Science and Information Engineering, 2009 WRI World Congress on, 2009, pp. 191195.

[5] D. Hou, X. Song, G. Zhang, H. Zhang and H. Loaiciga, "An early warning and control system for urban, drinking water quality protection: China’s experience," Environmental Science and Pollution Research, vol. 20, pp. 4496-4508, 2013.

[6] BBC News of 08/03/2012 Hundreds of fish at Luton Hoo lakes killed by sewage [online] Accessed at:

[7]  A. Al Sayes, A. Radwan and L. Shakweer, "Impact of drainage water inflow on the environmental conditions and fishery resources of Lake Borollus," 2007. 

[8] V. Z. Antonopoulos and S. K. Gianniou, "Simulation of water temperature and dissolved oxygen distribution in Lake Vegoritis, Greece," Ecol. Model., vol. 160, pp. 39-53, 2003.

[9] P. J. Puri, M. Yenkie, D. Battalwar, N. V. Gandhare and D. B. Dhanorkar, "Study and Interpretation of Physico-Chemical Characteristic of Lake Water Quality in Nagpur City (India)," Rasayan J.Chemistry, vol. 3, pp. 800-810, 2010.

[10] Q. Fu, B. Zheng, X. Zhao, L. Wang and C. Liu, "Ammonia pollution characteristics of centralized drinking water sources in China," Journal of Environmental Sciences, vol. 24, pp. 1739-1743, 2012. 

[11] S. Hostetler, "Use of models and observations to assess trends in the 1950–2005 water balance and climate of Upper Klamath Lake, Oregon," Water Resour. Res., vol. 45, 2009. 

[12] A. Bhatnagar and P. Devi, "Applications of correlation and regression analysis in assessing lentic water quality: a case study at Brahmsarovar Kurukshetra, India." International Journal of Environmental Sciences, vol. 3, 2012. 

[13] G. L. Howick and J. Wilhm, "Turbidity in lake carl blackwell: Effects of water depth and wind," in Proc. Okla. Acad. Sci, 1985, pp. 51-57.

[14] The lake and Reservoir Restoration Guidance manual 1993 “Statistical methods for the analysis of lake water quality trends” Accessed online at 7.PDF on 30/04/2014

[15] L. Fu and Y. Wang, "Statistical Tools for Analyzing Water Quality Data," .

[16]  Li Ying, Zhou Jiti, Wang Xiangrui and Zhou Xiaohui, "Water quality evaluation of nearshore area using artificial neural network model," in Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on, 2009, pp. 1-4. 

[17] D. Ömer Faruk, "A hybrid neural network and ARIMA model for water quality time series prediction," Eng Appl Artif Intell, vol. 23, pp. 586594, 2010.