PYTHON-DRIVEN INNOVATIONS IN WATER ANALYSIS: INTEGRATING MACHINE LEARNING AND SENSOR NETWORKS FOR REAL-TIME WATER QUALITY MONITORING (original) (raw)

This paper explores the integration of Python programming with machine learning algorithms and sensor networks for real-time water quality monitoring. Python's versatile libraries and frameworks enable the development of intelligent systems that can process and analyze large volumes of water quality data, providing timely insights for effective water resource management. The paper synthesizes recent advancements in IoT, edge computing, and deep learning to showcase Python's potential in creating scalable and adaptive water analysis solutions. It delves into the challenges associated with data quality, sensor calibration, and system interoperability, offering insights into future research directions. The paper also presents practical code examples demonstrating data acquisition from water quality sensors, anomaly detection using unsupervised learning, and water quality prediction using Long Short-Term Memory (LSTM) networks. By leveraging Python's capabilities, water professionals can develop powerful tools for real-time monitoring, early warning systems, and predictive maintenance, ultimately contributing to the sustainable management of water resources.