Thesis Abstract

Abstract
The rising concerns over environmental pollution have prompted the exploration of innovative approaches to predict and mitigate detrimental impacts. This thesis investigates the utilization of Artificial Intelligence (AI) in predicting environmental pollution levels, aiming to enhance forecasting accuracy and proactive decision-making. The study delves into the application of AI techniques, including machine learning algorithms and data analytics, to analyze historical pollution data and identify patterns for predictive modeling. Through a comprehensive literature review, the research synthesizes existing knowledge on environmental pollution prediction methods and AI technologies, highlighting gaps and opportunities for improvement. The research methodology section outlines the data collection process, feature selection techniques, model development, and evaluation criteria employed in the study. Leveraging a diverse dataset of pollution variables, meteorological conditions, and geographical factors, the AI models are trained and tested to forecast pollution levels across different scenarios. The findings of the study reveal the efficacy of AI in predicting environmental pollution levels, demonstrating superior accuracy compared to traditional forecasting methods. Through a detailed discussion of the results, the thesis explores the strengths and limitations of AI models, as well as potential enhancements for future research. In conclusion, the significance of integrating AI technologies in environmental pollution prediction is underscored, emphasizing the benefits of proactive monitoring and timely interventions. The study contributes to the advancement of predictive modeling in environmental science and underscores the importance of leveraging AI for sustainable environmental management. The thesis concludes with a summary of key findings, implications for practice, and recommendations for further research in the field of AI-driven pollution prediction.

Thesis Overview