Utilizing Machine Learning within Artificial Intelligence to Enhance Dissolved Oxygen Estimation in the Mississippi River via Temperature-Driven Polynomial Regression

Abstract

Abstract: In this research, a comprehensive examination of Dissolved Oxygen (DO) levels in the Mississippi River is undertaken, employing a Polynomial Regression model drivenby temperature data for predictive estimation. Dissolved oxygen serves as a pivotal indicator of water quality and the health of aquatic ecosystems, making its accurate forecasting crucial for effective environmental monitoring and management. By utilizingtemperature as a primary predictor, this study seeks to advance our understanding of the intricate relationship between temper- ature and DO within the context of the Mississippi River. The research involves an extensive dataset comprising measurements of water temperature and dissolved oxygen collected over an extended duration. A Polynomial Regression model is employed to establish a mathematical link between temperature and DO, thus providing a predictive tool for estimating DO levels at specific locations along the river. The model’s performance is subjected to a rigorous evaluation, involving the assessment of diverse statistical metrics and validation techniques. The research outcomes yield valuable insights into the dynamics of DO in the Mississippi River, emphasizing the pivotal role of temperature as a primary driver of DO fluctuations. This study introduces a practical and efficient method for the monitoring and prediction of DO levels, which can be instrumental in the preservation and sustainable management of this vital aquatic ecosystem. Moreover, it makes a meaningful contribution to the broader realm of water quality assessment and has the potential to inform policies and practices aimed at ensuring the environmental well- being of the Mississippi River.