A new Dataset for Detection of Illegal or Suspicious Spilling in Wastewater through Low-cost Real-time Sensors

Molinara, Mario; Bourelly, C.; Ferrigno, Luigi; Gerevini, Luca; Vitelli, Michele; Ria, Andrea; Magliocca, F.; Ruscitti, Luca; Simmarano, R.; Trynda, Anna; Olejnik, P.

doi:10.1109/smartcomp55677.2022.00070

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, the composition of WW is not stable over time, for example, due to atmospheric events such as rain. In detail, all the samples were acquired between 2019 and 2021 in two different laboratories in Poland and in Italy and were recently made public [9]. Table 2 reports the substances used.…”

Section: A Substancesmentioning

confidence: 99%

“…The effectiveness of the proposed classifier is tested against a set of state-of-the-art baselines on a dataset created in collaboration with Sensichips s.r.l. and made available to the scientific community [9]. Results show that the proposed methodology outperforms the baseline methods and its effectiveness allows for practical usage of the developed methodology.…”

mentioning

confidence: 93%

See 1 more Smart Citation

Detection of Wastewater Pollution Through Natural Language Generation With a Low-Cost Sensing Platform

et al. 2023

View full text Add to dashboard Cite

The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel classification approach to classify contaminants in wastewater, based on deep learning and the transformation of raw sensor data into natural language metadata. The proposed solution presents clear advantages against state-of-the-art systems in terms of higher effectiveness and reasonable efficiency. The main disadvantage of the proposed approach is that it relies on knowing the injection time, i.e., the instant in time when the contaminant is injected into the wastewater. For this reason, the developed system also includes a finite state machine tool able to infer the exact time instant when the substance is injected. The entire system is presented and discussed in detail. Furthermore, several variants of the proposed processing technique are also presented to assess the sensitivity to the number of used samples and the corresponding promptness/computational burden of the system. The lowest accuracy obtained by our technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best baseline method.

show abstract

Section: A Substancesmentioning

confidence: 99%

mentioning

confidence: 93%