Support Vector Machine for Predicting BOD of Greywater
Support Vector Machine for Predicting BOD of Greywater
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Samir Sadik Shaikh, Rekha Shahapurkar
|DOI : 10.14445/22315381/IJETT-V70I7P215|
How to Cite?
Samir Sadik Shaikh, Rekha Shahapurkar, "Support Vector Machine for Predicting BOD of Greywater" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 140-146, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P215
Greywater reuse has been a time-honored tradition for decades and benefits most regions facing tremendous water scarcity. Greywater has a large-scale potential for reuse and a wide area of application. The water quality index (WQI) determines the system of reuse and area of application for greywater. Various models can be found in earlier literature to predict WQI, namely the values of TSS, TDS, TS, pH, COD, and BOD based on ANN, DNN, SVM, KNN, and other approaches. Recently, some researchers have also proposed Water quality monitoring techniques based on IoT. This study's objective is to establish an indirect way of estimating the major wastewater quality parameters constructed on machine learning techniques. SVR, a version of SVM, was used to implement the model based on the kernel trick in PYTHON. The mean squared error function is prepared to analyze the model's overall effectiveness, which was observed to be 0.514. It was also observed that the mean squared error resulted from the cost of error (C). As the value of C increases, the hyperplane becomes much smoother, thereby improving the model's performance.
BOD, Error function, Greywater, Hyperplane, SVR.
 Casanova LM, Gerba CP, Karpiscak M, “ Chemical and Microbial Characterization of Household Greywater,” Journal of Environmental Science and Health, Part A. Toxic/Hazardous Substances and Environmental Engineering, Vol.34, Pp. 395–401, 2001.
 Ledin A, Eriksson E, Henze M, “Aspects of Groundwater Recharge Using Grey Wastewater, “ In: Lens P, Zeeman G, Lettinga G (Eds) Decentralised Sanitation and Reuse: Concepts, Systems and Implementation IWA Publishing, London, Pp. 354–370, 2001.
 Ottoson J, Stenstrom TA, “Fecal Contamination of Greywater and Associated Microbial Risks, “Water Research, Vol.37, Pp. 645– 655, 2003.
 Abedin SB, Rakib ZB, “Generation and Quality Analysis of Greywater At Dhaka City,” Environmental Research, Engineering, and Management, Vol. 64, Pp. 29–41, 2013.
 De Aguir Do Couto E, Calijuri ML, Assemany PP, Da Fonseca Santiago A, De Castro Carvalho I. “Greywater Production In Airports: Qualitative and Quantitative Assessment Resources,” Conservation and Recycling, Vol.77, Pp. 44–51, 2013.
 Katukiza AY, Ronteltap M, Niwagaba CB, Kansiime F, Lens PNL, “Grey Water Characterization and Pollutant Loads In An Urban Slum,” International Journal of Environmental Science and Technology, Vol.12, Pp. 423–436, 2014.
 Hernandez Leal L, Temmink H, Zeeman G, Buisman C, “ Comparison of Three Systems For Biological Greywater Treatment, Water,” Vol.2, Pp.155–169, 2010.
 Jamrah A, Al Omari A, Al Qasem L, Abdel Ghani N, “ Assessment of Availability and Characteristics of Greywater In Amman,” Water Science and Technology, Vol.50, Pp.157–164, 2011.
 H.Z. Abyaneh, “Evaluation of Multivariate Linear Regression and Artificial Neural Networks In Prediction of Water Quality Parameters,” J. Environ. Health Sci. Eng, Pp.12–40, 2015.
 J.S. Chou, C.C. Ho, H.S. Hoang, “Determining Quality of Water In Reservoir Using Machine Learning,” Ecol. Inform, Vol.44, Pp. 57– 75, 2018.
 R. Mohammadpour, S. Shaharuddin, C.K. Chang, N.A. Zakaria, A. Ab Ghani, N.W. Chan, “Prediction of Water Quality Index In Constructed Wetlands Using Support Vector Machine,” Environ. Sci. Pollut. Res, Vol.22, No.8, Pp.6208–6219, 2015.
 Muharemi, D. Logof˘atu, C. Andersson, F. Leon, “Approaches To Building A Detection Model For Water Quality: A Case Study, In Modern Approaches For Intelligent Information and Database Systems,” Springer, Pp. 173–183, 2018.
 U. Shafi, R. Mumtaz, H. Anwar, A.M. Qamar, H. Khurshid, “Surface Water Pollution Detection Using Internet of Things,” In Proceedings of 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & Iot, Pakistan, Vol.8, No.10, Pp. 92–96, 2018.
 Y. Xiang, L. Jiang, “Water Quality Prediction Using LSSVM and Particle Swarm Optimization, Second International Workshop, Knowledge Discovery and Data Mining WKDD,” Pp. 900–904, 2009.
 Vapnik V. and Chervonenkis A., “on the Uniform Convergence of Relative Frequencies of Events To Their Probabilities,” In Th. Prob. and Its Applications, Vol.17, No.2, Pp.264—280, 1971.
 Vapnik V., “Statistical Learning Theory,” Wiley, New York, 1998.
 Vayatis N. and Azencott R., "How To Estimate the Vapnik-Chervonenkis Dimension of Support Vector Machines Through Simulations," ACAI99, 1999.
 Wahba G., “Splines Models For Observational Data,” Series In Applied Mathematics, SIAM, Vol.59.
 Bartlett P. and Shawe-Taylor J., “Generalization Performance of Support Vector Machine and Other Pattern Classifiers,” In C.~Burges B.~Scholkopf, Editor, “Advances In Kernel Methods--Support Vector Learning.” MIT Press, 1998.
 Burges C., “A Tutorial on Support Vector Machines For Pattern Recognition,” In “Data Mining and Knowledge Discovery,” Kluwer Academic Publishers, Boston, Vol.2 ,1998.
 Evgeniou T., Pontil M., and Poggio T., “A Unified Framework For Regularization Networks and Support Vector Machines” A.I. Memo No. 1654, Artificial Intelligence Laboratory, MIT, 1999.
 Trafalis T., "Primal-Dual Optimization Methods In Neural Networks and Support Vector Machines Training," ACAI99, 1999.
 Evgeniou T, Pontil M., “Support Vector Machines: Theory and Applications,” Conference: Machine Learning and Its Applications, Advanced Lectures, 2001.
 Yaguo Lei, “Chapter 3 Individual Intelligent Method-Based Fault Diagnosis, In Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery,” Science Direct, Pp. 67-174, 2017.