Detection of Anomalous Pollution Sensors Using Deep Learning Strategies

Abstract

In recent years, the pollution problem has gained great importance due to its socioeconomic implications for people regarding health or logistic issues. The pollution level classically is measured with specialized expensive detectors located in some few locations. In the case of Temuco city there are three such centralized pollution monitoring stations. An alternative approach for measuring the pollution level of cities makes use of inexpensive pollution sensors located on public transportation vehicles. Nonetheless, a drawback of this approach is that these inexpensive sensors can be sensitive to noise, vehicle movement, human intervention or technical failures. Therefore, it is relevant to be able to automatically detect inaccurate or failing sensors as they are multiple and cannot be submitted frequently to a technical revision. In this work, we propose a method to automatically detect these anomalous sensors by an unsupervised deep learning approach using autoencoders. This work is part of an ongoing project where massive data are not still available. In this context, the simulated output of mobile pollution sensors is generated by a time series model that systematically inserts outlier measurements. Our results indicate that the proposed detection method is able to reliably reproduce the data generated and to detect the simulated outliers with an accuracy of over 95%. A post-publication change was made to this article on 3 Jul 2020 to correct an author name.

Cost Function Analysis Applied to Different Kinetic Release Models of Arrabidaea chica Verlot Extract from Chitosan/Alginate Membranes

“…The objective of this analysis is to minimize this difference, that is, to find the parameters that allow the model to fit the data as closely as possible [ 11 ]. The cost function can be performed for any type of mathematical model and compared with various experimental data; regarding numerical optimization methods, local gradient methods such as conjugate gradients [ 12 , 13 ] or global parameter population methods such as genetic algorithms [ 11 , 14 ] or deep learning strategies [ 15 ] can be applied.…”