Abstract

The decline in water conditions contributes to the crisis in clean water biodiversity. The interactions between water conditions indicators and the correlations among these variables and taxonomic groupings are intricate in their impact on biodiversity. However, since there are just a few kinds of Internet of Things (IoT) that are accessible to purchase, many chemical and biological measurements still need laboratory studies. The newest progress in Deep Learning and the IoT allows for the use of this method in the real-time surveillance of water quality, therefore contributing to preserving biodiversity. This paper presents a thorough examination of the scientific literature about the water quality factors that have a significant influence on the variety of freshwater ecosystems. It selected the ten most crucial water quality criteria. The connections between the quantifiable and valuable aspects of the IoT are assessed using a Generalized Regression-based Neural Networks (G-RNN) framework and a multi-variational polynomial regression framework. These models depend on historical data from the monitoring of water quality. The projected findings in an urbanized river were validated using a combination of traditional field water testing, in-lab studies, and the created IoT-depend water condition management system. The G-RNN effectively differentiates abnormal increases in variables from typical scenarios. The assessment coefficients for the system for degree 8 are as follows: 0.87, 0.73, 0.89, and 0.79 for N-O3-N, BO-D5, P-O4, and N-H3-N. The suggested methods and prototypes were verified against laboratory findings to assess their efficacy and effectiveness. The general efficacy was deemed suitable, with most forecasting mistakes smaller than 0.3 mg/L. This validation offers valuable insights into IoT methods' usage in pollutants released observation and additional water quality regulating usage, specifically for freshwater biodiversity preservation.