Optimizing network sensors using unsupervised machine-learning approach to identify a pollutant source

Sidi Mohammed Alaoui; Khalifa Djemal; Ehsan Sedgh Gooya; Amir Ali Feiz; Ayman Alfalou; Pierre Ngae

doi:10.1117/12.3013419

7 June 2024 Optimizing network sensors using unsupervised machine-learning approach to identify a pollutant source

Sidi Mohammed Alaoui, Khalifa Djemal, Ehsan Sedgh Gooya, Amir Ali Feiz, Ayman Alfalou, Pierre Ngae

Proceedings Volume 13040, Pattern Recognition and Prediction XXXV; 130400N (2024) https://doi.org/10.1117/12.3013419
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

In this paper, we present an optimization methodology for reducing the number of sensors in an existing monitoring network. These sensors measure the concentration of pollutant gas in the air, in order to estimate the position and intensity of a pollutant source. Two statistical methods were used and compared. The first method is based on Hierarchical Agglomerative Clustering (HAC), and the second one is Self-Organized Maps (SOM). The aim is to regroup sensors of the same behavior, based on similarity measure; then, we keep only one sensor of each cluster. The methodology was tested on synthetic data, with Bayesian inference and Monte Carlo Markov Chain (MCMC) algorithm to identify the pollutant source position and intensity. Of 88 sensors in the initial network, the number was reduced to 21 by HAC and 27 by SOM. As for the identification, both methods had close estimation of the source position, however the SOM had better results in the estimation of the source intensity in general.

Citation Download Citation

Sidi Mohammed Alaoui, Khalifa Djemal, Ehsan Sedgh Gooya, Amir Ali Feiz, Ayman Alfalou, and Pierre Ngae "Optimizing network sensors using unsupervised machine-learning approach to identify a pollutant source", Proc. SPIE 13040, Pattern Recognition and Prediction XXXV, 130400N (7 June 2024); https://doi.org/10.1117/12.3013419

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