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

The detection of contaminants in several environments (e.g., air, water, sewage systems) is 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 acquire, pre-process, transmit data classify wastewater; novel classification approach wastewater, based deep learning transformation raw sensor into natural language metadata. proposed solution presents clear advantages against state-of-the-art systems terms higher effectiveness reasonable efficiency. disadvantage it relies knowing injection time, i.e., instant time when contaminant injected wastewater. For reason, developed system also includes finite state machine tool able infer exact substance injected. entire presented discussed detail. Furthermore, variants processing technique are assess sensitivity number used samples corresponding promptness/computational burden system. lowest accuracy obtained by our 91.4%, which significantly than 81.0% reached best baseline method.