Water monitoring systems continuously working ensure real-time pollutant detection capabilities according to their sensitivity and specificity. It is necessary to balance such features because, although being able to sense several substances is a desired feature, the reduction of false positives is a primary goal a classification system should have. High false positive makes the system unusable. The current solution enables a 24/7 service with a sampling rate equal to 0.6 Hz. Our goal is to limit false positives to 1 per day, thus achieving 99.99% accuracy at least. In this paper, we add a false positive reduction module to our pre-existent system, aiming to manage false positive boosters as sensor drift and signal oscillations. Obtained results, using a Multi Layer Perceptron classifier, confirm the false positive reduction while keeping high true positive rates.
A False Positive Reduction System for Continuous Water Quality Monitoring
Bria A.;Ferrigno L.;Gerevini L.;Marrocco C.;Molinara M.
;Cerro G.;Vitelli M.
2021-01-01
Abstract
Water monitoring systems continuously working ensure real-time pollutant detection capabilities according to their sensitivity and specificity. It is necessary to balance such features because, although being able to sense several substances is a desired feature, the reduction of false positives is a primary goal a classification system should have. High false positive makes the system unusable. The current solution enables a 24/7 service with a sampling rate equal to 0.6 Hz. Our goal is to limit false positives to 1 per day, thus achieving 99.99% accuracy at least. In this paper, we add a false positive reduction module to our pre-existent system, aiming to manage false positive boosters as sensor drift and signal oscillations. Obtained results, using a Multi Layer Perceptron classifier, confirm the false positive reduction while keeping high true positive rates.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
