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.
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