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This chapter presents a framework for forecasting air pollution levels and uncovering hidden patterns using machine learning and frequent pattern mining. We analyze over five years of hourly \(PM_{2.5}\) data from Japan’s Atmospheric Environmental Regional Observation System (AEROS). The dataset undergoes preprocessing, including handling missing values and normalizing sensor data. A long short-term memory (LSTM) model predicts future pollution levels across sensors. The forecasted data is then transformed into a transactional database, where hazardous pollution levels are identified using a predefined threshold value. The FP-growth algorithm is applied to extract recurring pollution patterns, highlighting critical pollution hotspots. These insights help policymakers develop effective pollution mitigation strategies.

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Discovering Futuristic Pollution Patterns Using Forecasting and Pattern Mining

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  • 发表日期：

    2025年

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  • Uday Kiran Rage

摘要

This chapter presents a framework for forecasting air pollution levels and uncovering hidden patterns using machine learning and frequent pattern mining. We analyze over five years of hourly \(PM_{2.5}\) data from Japan’s Atmospheric Environmental Regional Observation System (AEROS). The dataset undergoes preprocessing, including handling missing values and normalizing sensor data. A long short-term memory (LSTM) model predicts future pollution levels across sensors. The forecasted data is then transformed into a transactional database, where hazardous pollution levels are identified using a predefined threshold value. The FP-growth algorithm is applied to extract recurring pollution patterns, highlighting critical pollution hotspots. These insights help policymakers develop effective pollution mitigation strategies.