A new approach for wastewater treatment using predictive data mining - A comparative study

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In the field of science data mining plays a major role in solving complex real world problems. The proposed method uses the predictive approach to determine the quality of water. To carry out the work, waste water samples were collected from textile industries and a dataset was created. Initially, preprocessing of the sample dataset was carried out. Classification is performed with, Random forest and Random Trees. Mean square error and the mean absolute error values were computed and the results are tabulated. Based on this, decision can be made regarding the recycling of the treated water. With the result it is well evident that the proposed method is able to predict the quality in a better way.




  • Keywords

    Predictive Data Mining; Preprocessing; Random Forest; Random Trees; Wastewater Treatment.

  • References

      [1] Bartosz Szeląg, Krzysztof Barbusiński, Jan Studziński, Lidia Bartkiewicz,” Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods”. E3S Web of Conferences 22, 00174 (2017) https://doi.org/10.1051/e3sconf/20172200174.

      [2] Bharat B. Gulyani and Arshia Fathima,” Introducing Ensemble Methods to Predict the Performance of Waste Water Treatment Plants (WWTP)”. International Journal of Environmental Science and Development, Vol. 8, No. 7, July 2017 https://doi.org/10.18178/ijesd.2017.8.7.1004.

      [3] Carlos Marquez-Vera, Alberto Cano, Cristobal Romero, Sebastian Ventura, “Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data”. Springer Science Business Media, LLC 2012. https://doi.org/10.1007/s10489-012-0374-8.

      [4] Corominas Li, Garrido-Baserba.M, Olsson G, Cortes.U, Poch.M,” Transforming data into knowledge for improved waste water treatment operation: A critical review of techniques”. Environmental Modelling and Software, Elsevier. Scopus:85044678780. DOI: 10.1016/j.envsoft.2017.11.023. Publishing date: 2017-12-08. https://doi.org/10.1016/j.envsoft.2017.11.023.

      [5] Daniel Ribeiro, Antonio Sanfins, Orlando Belo,”Wastewater treatment plant performance prediction with Support Vector Machines”. ICDM’13 Proceedings of the 13th International conference on Advances in Data Mining: Applications and theoretical aspects, pages 99-111. Springer-verlag Berlin, Heidelberg 2013. ISBN: 978-3-642-39735-6, https://doi.org/10.1007/978-3-642-39736-3_8.

      [6] Davut Hanbay , Ibrahim Turkoglu , Yakup Demir, “Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks”. Expert Systems with Applications 34 (2008) 1038–1043. Available online at www.sciencedirect.com. https://doi.org/10.1016/j.eswa.2006.10.030.

      [7] M.Dixon, J.R.Gallop, S.C.Lambert, J.V.Healy, “Experience with Data Mining for the Anaerobic Wastewater Treatment Process”. Environmental Modelling and Software 22 (2007) 315-322. https://doi.org/10.1016/j.envsoft.2005.07.031.

      [8] Djeddou. M,Achour. B,” The Use Of A Neural Network Technique For The Prediction Of Sludge Volume Index In Municipal Wastewater Treatment Plant “Larhyss Journal, ISSN 1112-3680, 24, Décember 2015, pp. 351-370 © 2015 All rights reserved, Legal Deposit 1266-2002.

      [9] FatimetouZahra Mohamed Mahmoud, “The Application of Predictive Analutics: Benefits, Challenges and How it can be Improved”. International Journal of Scientific and Research Publications, Volume 7, Issue 5, May 2017. ISSN: 2250-3153.

      [10] Festim Halili, Avni Rustemi, “Predictive Modeling: Data Mining Regression Technique Applied in a Prototype”. International Journal of Computer Science and Mobile Computing, Vol.5 Issue 8, August -2016, Pg:207-215, ISSN: 2320-088x. www.ijcsmc.com. https://doi.org/10.5121/ijccms.2016.5301.

      [11] V.Kavya, S.Arumugam, “A Review on Predictive Analytics in Data Mining”. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.5, No.1/2/3, September 2016.

      [12] Manel Poch, Joaquim Comas,Jose Porro, Manel Garrido-Baserba Lluis Corominas, Maite Pijuan, “Where are we in Wastewater Treatment Plants Data Management? A Review and a Proposal”. International Environmental Modelling and Software Society (IEMSS), 7th Intl Congress on Env. Modelling and Software, San Diego, CA, USA, Daniel P.Ames, Nigel W.T.Quinn and Andrea E. Rizzoli(Eds). https://www.iemss.org/society/index.php/iemss-2014-proceedings.

      [13] P.Pandi Selvi,” Waste water treatment – An application of Predictive data mining”. Journal of Emerging Technologies and Innovative Research. Volume 5, Issue 9, September 2018, ISSN: 2349-5162. www.jetir.org.

      [14] Sakshi Rungta, Vanita Jain, Akanksha Utreja, “Data Mining Engine using Predictive Analytics”. International Journal of Computer Applications (0975 – 8887). Volume 121 – No.5, July 2015. https://doi.org/10.5120/21537-4545.

      [15] Shakuntala Jatav, Vivek Sharma, “An Algorithm for Predictive Data Mining Approach in Medical Diagnosis”. International Journal of Computer Science and Information Technology (IJCSIT) Vol 10, No 1, February 2018. https://doi.org/10.5121/ijcsit.2018.10102.

      [16] S.B.Soumya, N.Deepika, “Data Mining With Predictive Analytics for Financial Applications”. International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-1, January 2016. ISSN:2395-3470. www.ijseas.com.

      [17] C. Victoria Priscilla, A. Anusuya,” A Survey On Wastewater Treatment (Wwt) Analysis Using Various Techniques”. International Journal of Advanced Research in Computer Science (ISSN: 0976-5697). Volume 9, Special Issue No. 1, 2018. www.ijarcs.info.

      [18] http://www.statsoft.com/textbook/data-mining-techniques.

      [19] https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86.

      [20] https://water.usgs.gov/edu/wuww.html.

      [21] https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd.




Article ID: 29784
DOI: 10.14419/ijet.v7i2.32.29784

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.