AI-Powered Environmental Monitoring: Machine Learning Approaches For Air And Water Quality Assessment

Abstract

Environmental monitoring plays a crucial role in assessing the health of air and water systems, which are increasingly impacted by anthropogenic activities. Traditional monitoring methods, while effective, are often limited by spatial and temporal constraints, which can hinder real-time decision-making. In this study, we explore the potential of Artificial Intelligence (AI) and machine learning (ML) techniques for enhancing air and water quality assessment. A comprehensive evaluation of various ML models—including Random Forest, Support Vector Machine (SVM), Neural Networks, and K-Nearest Neighbors (KNN)—was conducted to assess their performance in predicting and classifying environmental quality metrics such as PM2.5 (air quality) and pH (water quality). Performance metrics such as accuracy, precision, recall, F1-score, and specificity were used to compare model efficacy.
The results indicated that Neural Networks performed robustly across multiple evaluation criteria, while SVM demonstrated high precision and specificity in certain cases. Time-series visualizations of air and water quality data over time were employed, revealing significant spatial and temporal variations in both parameters. Further, graphical analyses using pie charts, histograms, and box-and-whisker plots helped elucidate the distribution and variability of air and water quality levels, providing deeper insights into regional pollution trends. Radar graphs and surface plots illustrated the interplay between environmental factors, demonstrating how quality levels evolve spatially and temporally.
Overall, this study showcases the potential of AI-driven approaches for real-time environmental monitoring, offering insights that can guide policy-making and mitigation strategies. The findings suggest that AI models can not only improve the accuracy of environmental assessments but also support more proactive decision-making in the face of environmental challenges. Future research should explore the integration of additional environmental parameters and real-time deployment of these AI-based systems for broader-scale applications.