TRACKING AND FORECASTING HEAVY METAL WATER CONTAMINATION
Alt Text: Tracking and Forecasting Heavy Metal Water Contamination
Title: Tracking and Forecasting Heavy Metal Water Contamination
Caption: Developing a predictive model for monitoring and forecasting heavy metal water pollution.
Description: This research integrates advanced sensing technologies (ARIMA), data analytics, and predictive modeling to create a real-time monitoring system for heavy metal contamination in water. By leveraging machine learning, the study aims to detect, quantify, and forecast pollution events, enabling timely interventions and safeguarding water resources.
Keywords: Heavy Metal Water Contamination, Predictive Modeling, ARIMA, Environmental Monitoring, Water Quality Management
International Journal of Engineering and Techniques – Volume 10 Issue 3, June 2024
DVH Venu Kumar1, K. Bala Krishna2
1Assistant Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
2Assistant Professor, Department of Computer Science & Engineering, Geethanjali Institute of Science and Technology, Gangavaram, Andhra Pradesh, India.
Abstract
Water is vital for sustaining biodiversity and maintaining ecological balance. However, the deterioration of natural water bodies due to heavy metal contamination poses a serious threat to ecosystems and human health. This study introduces a real-time monitoring system utilizing advanced sensing technologies (ARIMA), predictive analytics, and machine learning for detecting and forecasting heavy metal pollution in lakes, streams, and estuaries. The model assesses pollution trends using Mean Squared Error, Root Mean Squared Error, and Regression Analysis, enabling timely interventions to safeguard water resources.
Keywords
Heavy Metal Water Contamination, Predictive Modeling, ARIMA, Environmental Monitoring, Water Quality Management
How to Cite
Venu Kumar, D.V.H., Bala Krishna, K., “Tracking and Forecasting Heavy Metal Water Contamination,” International Journal of Engineering and Techniques, Volume 10, Issue 3, June 2024. ISSN 2395-1303
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