Rivers Monitoring System Using Deep Learning Technique

Abstract

Purpose: The aquatic environment, including rivers and lakes, is crucial for the breeding and survival of fish and other animals.
 Findings: However, these environments are vulnerable to both intentional and unintentional environmental pollution, which can have detrimental effects on the ecosystem and its inhabitants.
 Methodology: Therefore, there is a pressing need for continuous monitoring of the aquatic environment to detect and address pollution in a timely manner. The challenge lies in establishing a monitoring system that can provide a continuous flow of information regarding the quality and health of the aquatic environment. Traditional monitoring methods often involve costly and time-consuming procedures, limiting their effectiveness in providing real-time data.
 Recommendations: To address this, there is a demand for low-cost and timely sensor technologies that can be deployed extensively to monitor various parameters of the aquatic environment, such as water quality, pollution levels, and ecosystem health. The proposed system one step for developing and implementation a low-cost sensor technology that are capable of monitoring various parameters of the aquatic environment. To be established a robust and scalable monitoring system that can handle large-scale deployment of sensors in rivers and lakes.