AI driven Environmental Monitoring: A Hybrid CNN- BiLSTM approach for Predicting Air Pollution

Abstract

The systematic process of observing and analysing environmental parameters like air, water, soil, climate and biodiversity using sensors, satellite systems, and analytical techniques to assess environmental condition is referred to as Environmental Monitoring. It plays a crucial role in understanding, controlling, and mitigating the impacts of climate change, ecosystem degradation, and pollution. But the growing complexity and volume of environmental data have exposed the limitations of traditional monitoring methods, which often rely on manual data collection and conventional statistical analysis. Thus, the integration of Artificial Intelligence (AI) has significantly enhanced the scope and effectiveness of environmental monitoring by enabling real-time data processing, predictive modelling, and intelligent decision-making. This paper presents a comprehensive study that combines a general overview of environmental monitoring with the application of AI techniques for air pollution prediction. A hybrid deep learning model based on Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed and implemented using a five-year air pollution dataset from Delhi. The CNN component captures spatial patterns and noise characteristics in pollutant data, while the BiLSTM model effectively learns temporal dependencies to improve prediction accuracy. The results demonstrate the superiority of the proposed model in forecasting air quality compared to conventional approaches.

The paper also highlights the alignment of AI-based environmental monitoring with key Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). By improving environmental prediction, resource management, and policy formulation, AI contributes significantly to sustainable development and climate resilience. Despite its advantages, challenges such as data availability, privacy concerns, and the need for skilled expertise remain critical considerations.