| Title |
A new Dataset for Detection of Illegal or Suspicious Spilling in Wastewater through Low-cost Real-time Sensors |
| ID_Doc |
44351 |
| Authors |
Molinara, M; Bourelly, C; Ferrigno, L; Gerevini, L; Vitelli, M; Ria, A; Magliocca, F; Ruscitti, L; Simmarano, R; Trynda, A; Olejnik, P |
| Title |
A new Dataset for Detection of Illegal or Suspicious Spilling in Wastewater through Low-cost Real-time Sensors |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/SMARTCOMP55677.2022.00070 |
| Abstract |
The spilling of suspicious or illegal substances in wastewater poses a serious global threat to human health. Low-cost sensor technologies enabling wastewater continuous monitoring are an important tool that can help face this problem. In this paper, electrical impedance measurements on different sensors are proposed in order to have a dataset of raw data to be used to perform the classification of possible contaminants in a water environment. In detail, the sensor technology is based on a proprietary multi-sensing platform called SENSIPLUS, which is arranged in a suitable set-up able to carry out measurements in water for prolonged times. Sensors metalized with different materials are jointly used to exploit sensitivity diversity to different contaminants. An ad-hoc measurement procedure has been designed, including data acquisition during the warm-up period, contaminant injection, and steady-state conditions. The dataset proposed in this paper has been acquired in different European laboratories (Italy and Poland) and is made publicly available for testing new data analysis or machine learning techniques for the detection and classification of ten wastewater dangerous "contaminants" (https://aida.unicas.it/icprchallenge2022/). |
| Author Keywords |
Wastewater; Spilling Detection; Machine Learning; Pollution Monitoring; Smart City; IoT |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000853867900054 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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