@article{(Open Science Index):https://publications.waset.org/pdf/10011922,
	  title     = {A Machine Learning Approach for Anomaly Detection in Environmental IoT-Driven Wastewater Purification Systems},
	  author    = {Giovanni Cicceri and  Roberta Maisano and  Nathalie Morey and  Salvatore Distefano},
	  country	= {},
	  institution	= {},
	  abstract     = {The main goal of this paper is to present a solution
for a water purification system based on an Environmental Internet
of Things (EIoT) platform to monitor and control water quality
and machine learning (ML) models to support decision making
and speed up the processes of purification of water. A real case
study has been implemented by deploying an EIoT platform and a
network of devices, called Gramb meters and belonging to the Gramb
project, on wastewater purification systems located in Calabria,
south of Italy. The data thus collected are used to control the
wastewater quality, detect anomalies and predict the behaviour of
the purification system. To this extent, three different statistical and
machine learning models have been adopted and thus compared:
Autoregressive Integrated Moving Average (ARIMA), Long Short
Term Memory (LSTM) autoencoder, and Facebook Prophet (FP).
The results demonstrated that the ML solution (LSTM) out-perform
classical statistical approaches (ARIMA, FP), in terms of both
accuracy, efficiency and effectiveness in monitoring and controlling
the wastewater purification processes.},
	    journal   = {International Journal of Environmental and Ecological Engineering},
	  volume    = {15},
	  number    = {3},
	  year      = {2021},
	  pages     = {123 - 130},
	  ee        = {https://publications.waset.org/pdf/10011922},
	  url   	= {https://publications.waset.org/vol/171},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 171, 2021},
	}