DATA SCIENCE IN ENVIRONMENTAL MONITORING: INTEGRATING MACHINE LEARNING FOR PREDICTIVE ANALYSIS
Keywords:
data science, environmental monitoring, machine learning, predictive analytics, air quality modeling, water quality assessmentAbstract
Environmental monitoring plays a vital role in assessing ecosystem health, managing natural resources, and mitigating climate-related risks. Traditional monitoring systems often rely on manual sampling and static analytical models, which limit their ability to provide timely and accurate predictions. The integration of data science and machine learning has transformed environmental monitoring by enabling automated data processing, real-time analysis, and predictive modeling. This study examines how modern machine learning techniques enhance environmental data interpretation and forecasting. It reviews key algorithms, data sources, and system architectures applied in air, water, andbiodiversity monitoring. Furthermore, the paper discusses challenges related to data quality, model interpretability, and infrastructure limitations. By highlighting recent advances and practical case studies, this research demonstrates the potential of data-driven approaches in supporting evidence-based environmental management and sustainable policy development.
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Copyright (c) 2025 Muhammad Saad Khan, Ayesha Malik, Bilal Ahmed
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