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Water Quality Prediction Based on an Improved ARIMA- RBF Model Facilitated by Remote Sensing Applications

  • Jiying Qie
  • Jiahu Yuan
  • Guoyin Wang
  • Xuerui Zhang
  • Botian Zhou
  • Weihui Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

Remote sensing technique are great used to assess and monitor water quality. An efficient and comprehensive method in monitoring water quality is of great demand to prevent water pollution and to mitigate the adverse impact on the livestock and crops caused by polluted water. This study focused on a typical water area, where eutrophication is the main problem, and thus, the total nitrogen was chosen as an important parameter for this study. The research contains two parts. The first part is the methodology development, an algorithms was proposed to inverse the total nitrogen (TN) concentrations from the field imagery acquisition. The squared correlation coefficient between the inversion values and measured values was 0.815. The second part is the deduction of water quality parameter (TN) from upstream to downstream. An improved hybrid model of Autoregressive Integrated Moving Average (ARIMA) model and Radial basis function neural network (RBF-NN) was developed to simulate and forecast variation trend of the water quality parameter. We evaluated our method using data sets from satellite. Our method achieved the competing predicting performance in comparison with the state-of-the-art method on missing data completion and data predicting. Generally, the evaluation results indicated that the developed methods were successfully applied in forecasting the water quality parameters and filling in missing data which cannot be inversed in space by satellite images due to the cloud and mist interference, and were of promising accuracy.

Keywords

HJ-1 Water quality prediction Total nitrogen ARIMA Radial basis function Hybrid model 

Notes

Acknowledgements

This work is supported by National Science and Technology Major Project (2014ZX07104-006), the Hundred Talents Program of CAS (NO. Y21Z110A10).

References

  1. 1.
    Allan, M.G., Hamilton, D.P., Hicks, B.J., Brabyn, L.: Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand. Int. J. Remote Sens. 32, 2037–2055 (2011)CrossRefGoogle Scholar
  2. 2.
    Torbick, N., Hu, F., Zhang, J., Qi, J., Zhang, H., Becker, B.: Mapping chlorophyll-a concentrations in West Lake, China using Landsat 7 ETM+. J. Great Lakes Res. 34, 559–565 (2008)CrossRefGoogle Scholar
  3. 3.
    Qiao, P.L., Zhang, J.X., Lin, Z.J.: The application of remote sensing technique to monitoring and evaluating water pollution in the Shiyang river valley. Remote Sens. Land Resour. 4, 39–45 (2003)Google Scholar
  4. 4.
    Moustaka-Gouni, M., Vardaka, E., Michaloudi, E., Kormas, K.A., Tryfon, E., Mihalatou, H., et al.: Plankton food web structure in a eutrophic polymictic lake with a history in toxic cyanobacterial blooms. Limnol. Oceanogr. 51, 715–727 (2006)CrossRefGoogle Scholar
  5. 5.
    Ji, W., Wu, Y.: Jiangxi Poyang Lake National Natural Reserve Study. China Forestry Press, Beijing (2002)Google Scholar
  6. 6.
    Ji, W., Lin, W., Huang, X.: Wuhan East Lake Water Column Floating Particulate Organic Carbon, Nitrogen, Phosphorous Decade Dynamic. Science Press (1995)Google Scholar
  7. 7.
    Lin, W., Wang, J.: Research of Wuhan East Lake Phosphorous Nutritional Status, pp. 108–128. Science Press (1995)Google Scholar
  8. 8.
    Wang, Y., Jiao, N.Z.: Research progresses in nutrient bottom-up effect on phytoplankton growth. Mar. Sci. Qingdao Chin. Ed. 24, 30–32 (2000)Google Scholar
  9. 9.
    Qiong, C.: The influence to water bloom by nitrogen, phosphorous. Bull. Biol. 41(5), 12–14 (2006)Google Scholar
  10. 10.
    Yang, L., Qin, B., Chen, F., et al.: Eutrophication Mechanisms and control technology and its applications. Chin. Sci. Bull. 51(16), 1857–1866 (2006)Google Scholar
  11. 11.
    Chau, K.W.: A review on integration of artificial intelligence into water quality modelling. Mar. Pollut. Bull. 52, 726–733 (2006)CrossRefGoogle Scholar
  12. 12.
    Zou, X., Wang, G., Gou, G., Li, H.: A divide-and-conquer method based ensemble regression model for water quality prediction. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS, vol. 8171, pp. 397–404. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Kumar, M., Anand, M.: An application of time series arima forecasting model for predicting sugarcane production in India. Stud. Bus. Econ. 9, 81–94 (2014)Google Scholar
  14. 14.
    Kavasseri, R.G., Seetharaman, K.: Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy 34, 1388–1393 (2009)CrossRefGoogle Scholar
  15. 15.
    Sun, H., Koch, M.: Time series analysis of water quality parameters in an estuary using Box-Jenkins ARIMA models and cross correlation techniques. Comput. Methods Water Resour. 11, 230–239 (1996)Google Scholar
  16. 16.
    Tang, Z., de Almeida, C., Fishwick, P.A.: Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation 57, 303–310 (1991)CrossRefGoogle Scholar
  17. 17.
    Tang, Z., Fishwick, P.A.: Feedforward neural nets as models for time series forecasting. ORSA J. Comput. 5, 374–385 (1993)CrossRefGoogle Scholar
  18. 18.
    Zhang, G.P., Patuwo, B.E., Hu, M.Y.: A simulation study of artificial neural networks for nonlinear time-series forecasting. Comput. Oper. Res. 28, 381–396 (2001)CrossRefGoogle Scholar
  19. 19.
    Chang, T.S.: A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Syst. Appl. 38, 14846–14851 (2011)CrossRefGoogle Scholar
  20. 20.
    Al-Saba, T., El-Amin, I.: Artificial neural networks as applied to long-term demand fore-casting. Artif. Intell. Eng. 13, 189–197 (1999)CrossRefGoogle Scholar
  21. 21.
    Hwarng, H.B.: Insights into neural-network forecasting of time series corresponding to ARMA (p, q) structures. Omega 29, 273–289 (2001)CrossRefGoogle Scholar
  22. 22.
    Zhang, G., Hu, M.Y.: Neural network forecasting of the British Pound/US Dollar exchange rate. Omega 26, 495–506 (1998)CrossRefGoogle Scholar
  23. 23.
    Hatzikos, E., Anastasakis, L., Bassiliades, N., Vlahavas, I.: Simultaneous prediction of multiple chemical parameters of river water quality with tide. In: Proceedings of the Second International Scientific Conference on Computer Science, IEEE Computer Society, Bulgarian Section (2005)Google Scholar
  24. 24.
    Han, H.G., Chen, Q.L., Qiao, J.F.: An efficient self-organizing RBF neural network for water quality prediction. Neural Networks 24, 717–725 (2011)CrossRefGoogle Scholar
  25. 25.
    Yu, S., Zhu, K., Diao, F.: A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction. Appl. Math. Comput. 195, 66–75 (2008)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Kasabov, N.K., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10, 144–154 (2002)CrossRefGoogle Scholar
  27. 27.
    Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)CrossRefGoogle Scholar
  28. 28.
    Faruk, D.Ö.: A hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 23, 586–594 (2010)CrossRefGoogle Scholar
  29. 29.
    Taskaya-Temizel, T., Casey, M.C.: A comparative study of autoregressive neural network hybrids. Neural Networks 18, 781–789 (2005)CrossRefGoogle Scholar
  30. 30.
    Lathrop, R.G.: Use of Thematic Mapper data to assess water quality in Green Bay and central Lake Michigan. Photogramm Eng. Remote Sens. 52, 671–680 (1986)Google Scholar
  31. 31.
    Khashei, M., Bijari, M.: A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11, 2664–2675 (2011)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Jiying Qie
    • 1
  • Jiahu Yuan
    • 1
  • Guoyin Wang
    • 1
  • Xuerui Zhang
    • 1
  • Botian Zhou
    • 1
  • Weihui Deng
    • 1
  1. 1.Big Data Mining and Applications Center, Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina

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