A new approach for wastewater treatment using predictive data mining - A comparative study
Keywords:Predictive Data Mining, Preprocessing, Random Forest, Random Trees, Wastewater Treatment.
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.
 Bartosz SzelÄ…g, Krzysztof BarbusiÅ„ski, Jan StudziÅ„ski, Lidia Bartkiewicz,â€ Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methodsâ€. E3S Web of Conferences 22, 00174 (2017) https://doi.org/10.1051/e3sconf/20172200174.
 Bharat B. Gulyani and Arshia Fathima,â€ Introducing Ensemble Methods to Predict the Performance of Waste Water Treatment Plants (WWTP)â€. International Journal of Environmental Science and Development, Vol. 8, No. 7, July 2017 https://doi.org/10.18178/ijesd.2017.8.7.1004.
 Carlos Marquez-Vera, Alberto Cano, Cristobal Romero, Sebastian Ventura, â€œPredicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced dataâ€. Springer Science Business Media, LLC 2012. https://doi.org/10.1007/s10489-012-0374-8.
 Corominas Li, Garrido-Baserba.M, Olsson G, Cortes.U, Poch.M,â€ Transforming data into knowledge for improved waste water treatment operation: A critical review of techniquesâ€. 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.
 Daniel Ribeiro, Antonio Sanfins, Orlando Belo,â€Wastewater treatment plant performance prediction with Support Vector Machinesâ€. ICDMâ€™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.
 Davut Hanbay , Ibrahim Turkoglu , Yakup Demir, â€œPrediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networksâ€. Expert Systems with Applications 34 (2008) 1038â€“1043. Available online at www.sciencedirect.com. https://doi.org/10.1016/j.eswa.2006.10.030.
 M.Dixon, J.R.Gallop, S.C.Lambert, J.V.Healy, â€œExperience with Data Mining for the Anaerobic Wastewater Treatment Processâ€. Environmental Modelling and Software 22 (2007) 315-322. https://doi.org/10.1016/j.envsoft.2005.07.031.
 Djeddou. M,Achour. B,â€ The Use Of A Neural Network Technique For The Prediction Of Sludge Volume Index In Municipal Wastewater Treatment Plant â€œLarhyss Journal, ISSN 1112-3680, 24, DÃ©cember 2015, pp. 351-370 Â© 2015 All rights reserved, Legal Deposit 1266-2002.
 FatimetouZahra Mohamed Mahmoud, â€œThe Application of Predictive Analutics: Benefits, Challenges and How it can be Improvedâ€. International Journal of Scientific and Research Publications, Volume 7, Issue 5, May 2017. ISSN: 2250-3153.
 Festim Halili, Avni Rustemi, â€œPredictive Modeling: Data Mining Regression Technique Applied in a Prototypeâ€. 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.
 V.Kavya, S.Arumugam, â€œA Review on Predictive Analytics in Data Miningâ€. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.5, No.1/2/3, September 2016.
 Manel Poch, Joaquim Comas,Jose Porro, Manel Garrido-Baserba Lluis Corominas, Maite Pijuan, â€œWhere are we in Wastewater Treatment Plants Data Management? A Review and a Proposalâ€. 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.
 P.Pandi Selvi,â€ Waste water treatment â€“ An application of Predictive data miningâ€. Journal of Emerging Technologies and Innovative Research. Volume 5, Issue 9, September 2018, ISSN: 2349-5162. www.jetir.org.
 Sakshi Rungta, Vanita Jain, Akanksha Utreja, â€œData Mining Engine using Predictive Analyticsâ€. International Journal of Computer Applications (0975 â€“ 8887). Volume 121 â€“ No.5, July 2015. https://doi.org/10.5120/21537-4545.
 Shakuntala Jatav, Vivek Sharma, â€œAn Algorithm for Predictive Data Mining Approach in Medical Diagnosisâ€. International Journal of Computer Science and Information Technology (IJCSIT) Vol 10, No 1, February 2018. https://doi.org/10.5121/ijcsit.2018.10102.
 S.B.Soumya, N.Deepika, â€œData Mining With Predictive Analytics for Financial Applicationsâ€. International Journal of Scientific Engineering and Applied Science (IJSEAS) â€“ Volume-2, Issue-1, January 2016. ISSN:2395-3470. www.ijseas.com.
 C. Victoria Priscilla, A. Anusuya,â€ A Survey On Wastewater Treatment (Wwt) Analysis Using Various Techniquesâ€. International Journal of Advanced Research in Computer Science (ISSN: 0976-5697). Volume 9, Special Issue No. 1, 2018. www.ijarcs.info.
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