Water pollution is a big problem right now. A lot of people are not aware of it, but it is one of the biggest threats to our health and our environment. The ways that are usually employed to detect and measure the samples in laboratories in some place can take a lot of time and are very limiting in terms of the geographical areas in which they can be utilized. So, to tackle this tough challenge, DeepDiveMechanism has been developed—a system that not only detects pollution but also characterizes it with real-time data from the underwater environment.

In order to verify the results, the model was subjected to a simulation inspired by physics, which considered the diffusion of the pollutant in the water and interactions of the sensors under different water conditions. The pH, dissolved oxygen, turbidity, and conductivity were four parameters that were used for the monitoring, and the neural autoencoder, which was almost as light as a feather and performed the single task of isolating the anomalies, detected the pollutants. The aspects of the system performance were assessed through the precision–recall metrics, and the check-ups and the movement of the pollutants across different locations were visually represented, which were the main factors that reassured the system was capable of detecting the contamination effectively and in a timely manner.

The outcome is that DeepDiveMechanism can be counted on for marking water quality changes in a short time period and also in the presence of sensor drift and noise. It can be surmised from such statistically supported possibilities that it can be put to use in the real world, and its best will show in faraway places or regions with limited resources. The prospects for the future are open to having hyperspectral imaging integrated and using secure edge-cloud synchronisation for the more accurate classification of pollutants and the protection of the integrity of the data.