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Smart city article details

Title	Iot Regulated Water Quality Prediction Through Machine Learning For Smart Environments
ID_Doc	33881
Authors	Kaur E.
Year	2022
Published	Intelligent Systems Reference Library, 121
DOI	http://dx.doi.org/10.1007/978-3-030-97516-6_3
Abstract	Smart cities have been contingent on the concept of Internet of Things since the beginning, the only measurable gap is the means to achieve it. Smart cities are not limited to urban housing but are more sectarian in the suburbs and require a means to devise efficient mechanisms and systems that can support sustainable growth. Industrial suburbs are the major pollutants that are the primary focus of this research. This paper works on the data of recycled wastewater procured from industrial use, which otherwise is directly discharged into the rivers. We collect the data using IoT sensors, these sensors are responsible for obtaining information about the data, also for scrutinizing and maintaining the water quality. The data contains major features that signify and influence the water quality, these parameters are used for the calculation of the water quality index. Finally, we train and predict this quality index using 3 machine learning algorithms-Decision Tree, Random Forest, and Deep Neural Networks. The evaluation metrics consist of RMSE, MAD, MAPE, and R squared value to identify and determine the model with the best performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Blockchain; Decision trees; Deep neural networks; IoT; Machine learning; Random forests; Water quality index

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