Scholarlink

Accurate predictions of air quality are essential for both environmental protection and public health. Air pollution is a significant issue that affects our ability to evaluate air quality in relation to environmental and human health. Air pollution is one such menace that can have a bearing on the levels required to assess air cleanliness. Monitoring the atmosphere condition is essential for predicting air quality, which in turn impacts into public health. The Air Quality Index (AQI) is another kind of mechanism for monitoring air pollution that provides a general measure of air pollution. The AQI provides a comprehensive measure of the concentration of particulate pollution over a specific period and its effects on human health. Accurately predicting the AQI is crucial, especially given the complex nature of airborne particles. The AQI is an important tool for environmental and public health monitoring, and effective machine learning methods can significantly enhance by considering a wide range of air and meteorological factors. So, there introduces a novel approach to facing the intrinsic difficulty of predicting air quality hence, these are technique described generally as quantum machine learning since they have astonishing abilities for highly productive processing of very high-dimensional complex data structures with learning the set. This study offers a comparative analysis of classical and quantum models for predicting AQI using real air pollution datasets. The results indicate that classical models achieve approximately 90% accuracy, while QML-based models surpass 92% and show improved computational efficiency. In this research, an approach of a quantum machine-enhanced method to build a model around air quality prediction is based on AQI taking full advantage of unique quantum computing properties allowing much-prized patterns in the data that standard machine learning methods would overlook. In this approach, we are able to perform quantum feature mapping permitting the most efficient handling of the high-dimensional input space features of air quality data.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quantum Support Vector Machines for Environmental Data Analysis a Path Toward Efficient AQI Prediction

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Dayoniase Santhiyagu,
  • G. Nagarajan

摘要

Accurate predictions of air quality are essential for both environmental protection and public health. Air pollution is a significant issue that affects our ability to evaluate air quality in relation to environmental and human health. Air pollution is one such menace that can have a bearing on the levels required to assess air cleanliness. Monitoring the atmosphere condition is essential for predicting air quality, which in turn impacts into public health. The Air Quality Index (AQI) is another kind of mechanism for monitoring air pollution that provides a general measure of air pollution. The AQI provides a comprehensive measure of the concentration of particulate pollution over a specific period and its effects on human health. Accurately predicting the AQI is crucial, especially given the complex nature of airborne particles. The AQI is an important tool for environmental and public health monitoring, and effective machine learning methods can significantly enhance by considering a wide range of air and meteorological factors. So, there introduces a novel approach to facing the intrinsic difficulty of predicting air quality hence, these are technique described generally as quantum machine learning since they have astonishing abilities for highly productive processing of very high-dimensional complex data structures with learning the set. This study offers a comparative analysis of classical and quantum models for predicting AQI using real air pollution datasets. The results indicate that classical models achieve approximately 90% accuracy, while QML-based models surpass 92% and show improved computational efficiency. In this research, an approach of a quantum machine-enhanced method to build a model around air quality prediction is based on AQI taking full advantage of unique quantum computing properties allowing much-prized patterns in the data that standard machine learning methods would overlook. In this approach, we are able to perform quantum feature mapping permitting the most efficient handling of the high-dimensional input space features of air quality data.