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Integration of Machine Learning Augmented With Biosensors for Enhanced Water Quality Monitoring
Abstract
Monitoring water quality is essential to guaranteeing the sustainability and safety of water supplies. Conventional techniques for evaluating the quality of water might be laborious and may not be able to provide results instantly. The suggested system makes use of a wide range of biosensors to assess important aspects of water quality, including microbial activity, pH, dissolved oxygen, and chemical pollutants. Following collection, the data are analysed using recurrent neural networks (RNNs). An RNN is trained to identify patterns, correlate information from several sensors, and forecast trends in water quality. Early detection of problems with water quality, prompt reaction to possible contaminants, and flexibility in response to changing environmental conditions are some benefits of this integrated approach. The system biosensors for enhanced water quality monitoring (BEWQM) are a useful tool for long-term water quality monitoring and management because of its learning characteristics, which allow it to continuously improve its accuracy and performance over time.
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